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https://github.com/qdrant/landing_page.git
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Update Legal-tech page (#2329)
* Update Legal-tech page * combine all HTML files into one for industries * small fix * update hospitality-and-travel page * update e-commerce page * update images * update text and icon * update Semantic Search link * remove unused markdown --------- Co-authored-by: trean <trean.mi@gmail.com>
This commit is contained in:
nastyapash
@@ -1,12 +0,0 @@
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---
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title: AI-Powered Vector Search for Smarter Shopping & Personalized E-Commerce
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description: ''
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social_preview_image: /e-commerce/social-preview.png
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build:
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render: always
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cascade:
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- build:
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list: local
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publishResources: false
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render: never
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---
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@@ -1,35 +0,0 @@
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---
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title: E-commerce Challenges
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cards:
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- id: 0
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icon:
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src: /icons/outline/search-error.svg
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alt: Icon
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title: Invisible Products, Lost Sales
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description: Keyword-based search misses intent, leaving customers empty-handed.
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- id: 1
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icon:
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src: /icons/outline/slow-blue.svg
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alt: Icon
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title: Sluggish Search, Lost Patience
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description: Slow, clunky results drive users away-especially on mobile.
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- id: 2
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icon:
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src: /icons/outline/scale-error-purple.svg
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alt: Icon
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title: Off-Target, Off-Putting
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description: Generic, rule-based recommendations miss the mark, killing engagement.
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- id: 3
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icon:
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src: /icons/outline/hacker-purple.svg
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alt: Icon
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title: Fraud Runs Wild
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description: Fake accounts, high return rates, and shady transactions slip past outdated defenses.
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- id: 4
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icon:
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src: /icons/outline/label-teal.svg
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alt: Icon
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title: Pricing & Inventory Chaos
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description: Guesswork leads to overstocked shelves, lost revenue, and bad margins.
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sitemapExclude: true
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---
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@@ -1,23 +0,0 @@
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---
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title: Who We Help
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cards:
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- id: 0
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icon:
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src: /img/marketing-landings/shop.svg
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alt: shop icon
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title: Online Marketplaces
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description: Enhance search, recommendations, and fraud detection at scale.
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- id: 1
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icon:
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src: /img/marketing-landings/fashion.svg
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alt: fashion icon
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title: Fashion & Apparel Retailers
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description: AI-powered visual vector search for similar styles & personalized outfit recommendations.
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- id: 2
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icon:
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src: /img/marketing-landings/market.svg
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alt: market icon
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title: Supermarkets & Grocery Stores
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description: Smart promotions, demand forecasting, and inventory optimization.
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sitemapExclude: true
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---
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@@ -1,7 +0,0 @@
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---
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content: Transform Your E-Commerce <span class="text-nowrap">Strategy with AI & Vector Search</span>
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contactUs:
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text: Get Started
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url: /contact-us/
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sitemapExclude: true
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---
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@@ -1,10 +0,0 @@
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---
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title: Transform Your E-Commerce Strategy with AI & Vector Search
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button:
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text: Get Started
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url: https://cloud.qdrant.io/signup
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image:
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src: /img/rocket.svg
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alt: Rocket
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sitemapExclude: true
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---
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@@ -1,23 +0,0 @@
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---
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title: Qdrant's vector search powers various AI <span class="text-nowrap">e-commerce</span> applications
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items:
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- id: 0
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title: Personalized Recommendations
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description: Increase sales with AI-driven product suggestions tailored to each shopper.
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- id: 1
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title: Next-Gen Search & Discovery
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description: Deliver accurate vector-based search for products, images, and attributes.
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- id: 2
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title: Real-Time Fraud Detection
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description: Spot anomalies in transactions and returns before they impact your business.
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- id: 3
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title: Smart Inventory & Pricing
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description: Optimize stock levels and pricing strategies dynamically with AI insights.
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- id: 4
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title: Conversational Shopping Assistants
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description: Engage customers with AI chatbots that understand natural language.
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image:
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src: /img/e-commerce-search.png
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alt: Search
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sitemapExclude: true
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---
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@@ -1,19 +0,0 @@
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---
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tag:
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name: AI-Powered Shopping
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icon:
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src: /icons/outline/cart-teal.svg
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alt: Shopping Cart
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title: AI-Powered Vector Search for Smarter Shopping & Personalized E-Commerce
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description: Drive conversions with AI-powered vector search that delivers hyper-relevant product discovery, personalized recommendations, and fraud detection—all powered by Qdrant’s enterprise-grade vector search.
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startFree:
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text: Get Started
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url: https://cloud.qdrant.io/signup
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contactUs:
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text: Contact Us
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url: /contact-us/
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image:
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src: /img/e-commerce-hero.png
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alt: A smiling woman with a laptop and a bank card
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sitemapExclude: true
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---
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@@ -1,7 +0,0 @@
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---
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logos:
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- /img/customers-logo/kaufland.svg
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- /img/customers-logo/flipkart.svg
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- /img/customers-logo/meesho.svg
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sitemapExclude: true
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---
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||||
@@ -1,28 +0,0 @@
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---
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title: Why Choose Qdrant
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items:
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- id: 0
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icon:
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src: /icons/outline/growth-red.svg
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alt: Growing plot
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description: <strong>Real-Time Performance:</strong> Handle millions of products and transactions instantly.
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- id: 1
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icon:
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src: /icons/outline/integration-purple.svg
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alt: Graph
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description: <strong>Seamless Integration:</strong> Works with your existing e-commerce platform & infrastructure.
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- id: 2
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icon:
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src: /icons/outline/shield-blue.svg
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alt: Shield
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description: <strong>Enterprise-Grade Security:</strong> Keep customer data safe & compliant with GDPR standards.
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- id: 3
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icon:
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src: /icons/outline/cloud-connections-green.svg
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alt: Cloud connections
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description: <strong>Deploy your way:</strong> Use our managed cloud, hybrid cloud or private cloud. We support AWS, Azure, and GCP.
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image:
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src: /img/legal-tech-why-qdrant.svg
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alt: Why qdrant
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sitemapExclude: true
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---
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@@ -1,12 +0,0 @@
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---
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title: Hospitality & Travel
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description: Discover how Qdrant's vector search technology can transform the hospitality and travel industry by enhancing personalization, improving content discovery, and optimizing fraud detection.
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social_preview_image: /hospitality-and-travel/social-preview.png
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build:
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render: always
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cascade:
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- build:
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list: local
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publishResources: false
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render: never
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---
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@@ -1,35 +0,0 @@
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---
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title: New Vector Search Can Solve <br>Old Challenges
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cards:
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- id: 0
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icon:
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src: /icons/outline/search-error.svg
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alt: Icon
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title: Missed Matches, Lost Bookings
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description: Keyword-based search fails to connect travelers with the right destinations and stays.
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- id: 1
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icon:
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src: /icons/outline/bad-recommendation.svg
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alt: Icon
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title: One-Size-Fits-None
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description: Generic recommendations ignore personal preferences, leading to low engagement.
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- id: 2
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icon:
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src: /icons/outline/missing-review.svg
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alt: Icon
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title: Poor Content Discovery
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description: Reviews, guides, and user-generated insights remain buried, making decisions harder.
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- id: 3
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icon:
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src: /icons/outline/slow.svg
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alt: Icon
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title: Slow Search, Abandoned Carts
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description: Clunky, outdated search frustrates users and drives them elsewhere.
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- id: 4
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icon:
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src: /icons/outline/hacker.svg
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alt: Icon
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title: Fraud Eats Profits
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description: Fake bookings and transaction fraud slip through weak detection systems.
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sitemapExclude: true
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---
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@@ -1,23 +0,0 @@
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---
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title: Who We Help
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cards:
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- id: 0
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icon:
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src: /img/marketing-landings/online-booking.svg
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alt: Laptop with geo pin
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title: Online Travel Agencies & Booking Platforms
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description: AI-powered vector search for flights, hotels, and experiences.
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- id: 1
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icon:
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src: /img/marketing-landings/hotel.svg
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alt: Three hotel buildings
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title: Hotel Chains & Resorts
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description: Improve direct bookings and create tailored guest experiences.
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- id: 2
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icon:
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src: /img/marketing-landings/geo.svg
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alt: Earth with geo pin
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title: Tour & Event Marketplaces
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description: Help users discover the best experiences with vector-powered matching.
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sitemapExclude: true
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---
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@@ -1,7 +0,0 @@
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---
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content: Turn Travel Search into an AI-Driven Experience with Vector Search
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contactUs:
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text: Get Started
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||||
url: /contact-us/
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sitemapExclude: true
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---
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@@ -1,10 +0,0 @@
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---
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title: Turn Travel Search into an AI-Driven Experience with Vector Search
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button:
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text: Get Started
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||||
url: https://cloud.qdrant.io/signup
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image:
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src: /img/rocket.svg
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alt: Rocket
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sitemapExclude: true
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---
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@@ -1,23 +0,0 @@
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---
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||||
title: Qdrant's vector search powers various <br>AI travel applications
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items:
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||||
- id: 0
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||||
title: AI-Powered Travel Recommendations
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||||
description: Suggest destinations, hotels, and activities based on past bookings and preferences.
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||||
- id: 1
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||||
title: Smart Flight & Hotel Search
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||||
description: Help travelers find the best options with vector-based search for seamless booking.
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- id: 2
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||||
title: User-Generated Content Search
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||||
description: Enable deep semantic search across reviews, travel guides, and user-generated content.
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- id: 3
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||||
title: Fraud Prevention in Bookings
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||||
description: Detect and prevent unusual booking patterns and fraudulent transactions in real time.
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- id: 4
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||||
title: AI Chatbots & Virtual Assistants
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||||
description: Provide instant traveler support with intelligent chatbots.
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||||
image:
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||||
src: /img/travel-search.png
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||||
alt: Search
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||||
sitemapExclude: true
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||||
---
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@@ -1,19 +0,0 @@
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---
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||||
tag:
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||||
name: AI-Driven Travel & Booking
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||||
icon:
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||||
src: /icons/outline/plan.svg
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||||
alt: Airplane taking off
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||||
title: <span class="text-nowrap">AI-Driven Vector Search</span> <span class="text-nowrap">for Personalized Travel &</span> Booking Experiences
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||||
description: Build hyper-personalized travel experiences with Qdrant’s intelligent search—helping travelers find the perfect destination, stay, or itinerary with AI-powered vector search.
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||||
startFree:
|
||||
text: Get Started
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||||
url: https://cloud.qdrant.io/signup
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||||
contactUs:
|
||||
text: Contact Us
|
||||
url: /contact-us/
|
||||
image:
|
||||
src: /img/travel-hero.png
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||||
alt: A passenger is waiting to board, looking at his phone, with airplanes outside the window in the background
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||||
sitemapExclude: true
|
||||
---
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||||
@@ -1,11 +0,0 @@
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||||
---
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||||
names: Rahul Todkar
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||||
positions: Head of Data and AI, TripAdvisor
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||||
review: “Qdrant has been crucial for our transformation. When you're dealing with over a billion plus user-generated, multi-modal pieces of content from hundreds of millions of monthly active users across 21 countries, 11M businesses and all the complex user interactions that come with it, you need a way to bring it all together. Now, we can represent everything from hotel preferences to restaurant choices to user behavior in a unified way. And we’re seeing real business results. Users engaging with our AI-powered features like trip planning are showing 2-3x more revenue.”
|
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avatar:
|
||||
src: /img/customers/rahul-todkar.svg
|
||||
alt: Rahul Todkar Avatar
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||||
logo:
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||||
src: /img/brands/tripadvisor.svg
|
||||
alt: TripAdvisor Logo
|
||||
---
|
||||
@@ -1,28 +0,0 @@
|
||||
---
|
||||
title: Why Choose Qdrant
|
||||
items:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/custom-map-red.svg
|
||||
alt: Map with a pencil
|
||||
description: <strong>Real-Time Personalization:</strong> Deliver AI-driven travel experiences uniquely suited to each user.
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/scale-blue.svg
|
||||
alt: Four arrows pointing outwards
|
||||
description: <strong>Built for Scale:</strong> Handle global search queries with low latency.
|
||||
- id: 2
|
||||
icon:
|
||||
src: /icons/outline/integration-purple.svg
|
||||
alt: Graph
|
||||
description: <strong>Seamless Integration:</strong> Works with your existing booking and CRM systems.
|
||||
- id: 3
|
||||
icon:
|
||||
src: /icons/outline/cloud-connections-green.svg
|
||||
alt: Cloud connections
|
||||
description: <strong>Deploy your way:</strong> Use our managed cloud, hybrid cloud or private cloud. We support AWS, Azure, and GCP.
|
||||
image:
|
||||
src: /img/legal-tech-why-qdrant.svg
|
||||
alt: Why qdrant
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,19 +0,0 @@
|
||||
---
|
||||
title: What you can build with Qdrant
|
||||
image:
|
||||
src: /img/hr-tech/Diagram.png
|
||||
alt: Flow chart showing parse, search, rank, and match process
|
||||
useCases:
|
||||
- id: 0
|
||||
title: Modernize classic job search
|
||||
paragraphs:
|
||||
- "Keep existing Boolean/keyword patterns where needed while adding semantic relevance with hybrid search."
|
||||
- "Apply strict filters alongside semantic search (Vector + Filter) for precise hiring constraints."
|
||||
- "Maintain performance as data grows and traffic spikes."
|
||||
- id: 1
|
||||
title: Resume-based job matching
|
||||
paragraphs:
|
||||
- "What: users upload a CV and get job recommendations with no manual query input."
|
||||
- "Why retrieval matters: CVs are messy (titles and skills rarely match cleanly) so semantic similarity improves match quality and diversity while filters keep results valid."
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,8 +0,0 @@
|
||||
---
|
||||
title: Talk to an expert about HR marketplace retrieval.
|
||||
description: Get guidance on scaling search, matching, and recommendations beyond 20M vectors.
|
||||
button:
|
||||
text: Talk to Sales
|
||||
url: /contact-us/
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,7 +0,0 @@
|
||||
---
|
||||
title: "<span class='text-accent'>The problem:</span> recruitment data is fuzzy, but hiring constraints are strict"
|
||||
paragraphs:
|
||||
- "Recruitment search and matching must handle ambiguity (skills synonyms, inconsistent job titles, incomplete resumes), and enforce hard constraints (location, work authorisation, certifications, salary bands, categories)."
|
||||
- "\"Semantic match\" alone is insufficient if the candidate or job fails mandatory filters, and keyword-only search misses great matches when terminology doesn't line up. Additionally, job application numbers are increasing due to AI."
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,24 +0,0 @@
|
||||
---
|
||||
title: "Precision Hiring at Scale: Vector Search + Filters for HR Marketplaces"
|
||||
description: Upgrade classic keyword job search, power resume-based recommendations, and deliver "similar jobs" experiences using a Vector + Filter architecture designed for real-world constraints.
|
||||
values:
|
||||
- icon:
|
||||
src: /img/hr-tech/filter-blue.svg
|
||||
alt: Filter icon
|
||||
text: Advanced filters for precision hiring
|
||||
- icon:
|
||||
src: /img/hr-tech/rocket-violet.svg
|
||||
alt: Rocket icon
|
||||
text: Real-time matching speed
|
||||
- icon:
|
||||
src: /img/hr-tech/badge-dollar-teal.svg
|
||||
alt: Cost icon
|
||||
text: Scale without the cost spike
|
||||
startFree:
|
||||
text: Talk to an Expert
|
||||
url: /contact-us/
|
||||
image:
|
||||
src: /img/hr-tech/Med-Tech-Key-Visual.png
|
||||
alt: HR Tech illustration with hiring dashboard and candidate matching
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,11 +0,0 @@
|
||||
---
|
||||
names: Elvis Moraa
|
||||
positions: Engineering Lead, Pariti
|
||||
review: "Pariti used Qdrant's vector search capabilities to rank candidates, increasing the hiring fill rate from 20% to 48%, and decreasing candidate vetting time from 4 minutes to 1 minute, a 70% time-savings.<br><br>\"Qdrant is the last thing I worry about breaking.\""
|
||||
avatar:
|
||||
src: /img/hr-tech/image.svg
|
||||
alt: Elvis Moraa Avatar
|
||||
logo:
|
||||
src: /img/hr-tech/Pariti.svg
|
||||
alt: Pariti Logo
|
||||
---
|
||||
@@ -1,29 +0,0 @@
|
||||
---
|
||||
title: Why Qdrant for HR Tech
|
||||
items:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /img/hr-tech/filter-icon.svg
|
||||
alt: Filter icon
|
||||
title: Advanced Filters for Precision Hiring
|
||||
description: With Qdrant, strict filters can be applied alongside semantic search without sacrificing performance, enabling real hiring workflows (location, certifications, categories, salary bands).
|
||||
- id: 1
|
||||
icon:
|
||||
src: /img/hr-tech/rocket-icon.svg
|
||||
alt: Rocket icon
|
||||
title: Engineered for Real-Time Matching & Speed
|
||||
description: In recruitment, speed is a competitive advantage. Qdrant's architecture is built to keep ranking responsive and reduce the "latency spike" behavior teams encounter in legacy setups.
|
||||
- id: 2
|
||||
icon:
|
||||
src: /img/hr-tech/chart-icon.svg
|
||||
alt: Chart icon
|
||||
title: Scalability Without Cost Spikes
|
||||
description: Scale from millions to tens of millions of vectors with cost-conscious optimization options (e.g., quantization) while maintaining throughput.
|
||||
- id: 3
|
||||
icon:
|
||||
src: /img/hr-tech/search-check-icon.svg
|
||||
alt: Search check icon
|
||||
title: Handling Fuzziness by Design
|
||||
description: Job titles and skill taxonomies are inconsistent, but Qdrant surfaces strong matches even when terminology doesn't line up with semantic similarity and recommendations.
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -0,0 +1,414 @@
|
||||
---
|
||||
title: AI-Powered Vector Search for Smarter Shopping & Personalized E-Commerce
|
||||
description: ''
|
||||
url: /e-commerce/
|
||||
social_preview_image: /e-commerce/social-preview.png
|
||||
sections:
|
||||
#hero-section
|
||||
- type: hero
|
||||
badge:
|
||||
title: E-commerce
|
||||
icon:
|
||||
src: /icons/outline/shopping-cart-blue.svg
|
||||
alt: Shopping cart
|
||||
title: Don’t Accept Slow Search
|
||||
description: Slow search, results missing intent, and infrastructure that can't handle traffic spikes shouldn’t be expected.<br>Qdrant enables fast, accurate results.
|
||||
containedButton:
|
||||
text: Talk to an Expert
|
||||
url: /contact-us/
|
||||
outlinedButton:
|
||||
text: Read the Docs
|
||||
url: /documentation/
|
||||
language: Python
|
||||
code: |
|
||||
# Hybrid search with business-logic reranking
|
||||
results = client.query_points(
|
||||
collection_name="products",
|
||||
prefetch=[
|
||||
Prefetch(query=dense_emb, using="dense", limit=100),
|
||||
Prefetch(query=sparse_emb, using="sparse", limit=100),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("in_stock", match=MatchValue(True)),
|
||||
FieldCondition("category", match=MatchValue("shoes")),
|
||||
FieldCondition("price", range=Range(lte=150.0)),
|
||||
]),
|
||||
limit=20,
|
||||
)
|
||||
steps:
|
||||
- id: 0
|
||||
title: Step 1
|
||||
description: Embed - Parse + Embed Document
|
||||
icon:
|
||||
src: /icons/outline/square-code.svg
|
||||
alt: Code
|
||||
- id: 1
|
||||
title: Step 2
|
||||
description: Search - Semantic Search + Strict Filter
|
||||
icon:
|
||||
src: /icons/outline/filter-blue-small.svg
|
||||
alt: Filter
|
||||
- id: 2
|
||||
title: Step 3
|
||||
description: Rank - Rank + Rerank (Optional)
|
||||
icon:
|
||||
src: /icons/outline/list.svg
|
||||
alt: List
|
||||
- id: 3
|
||||
title: Step 4
|
||||
description: Result - Evidence-based Match
|
||||
icon:
|
||||
src: /icons/outline/circle-check.svg
|
||||
alt: Check
|
||||
badges:
|
||||
- id: 0
|
||||
title: Predictable low latency
|
||||
icon:
|
||||
src: /icons/outline/rocket-green.svg
|
||||
alt: Rocket
|
||||
- id: 1
|
||||
title: Hybrid Search
|
||||
icon:
|
||||
src: /icons/outline/locate-fixed-blue.svg
|
||||
alt: Locate fixed
|
||||
- id: 2
|
||||
title: Multimodal Search
|
||||
icon:
|
||||
src: /icons/outline/image-blue.svg
|
||||
alt: Image
|
||||
- id: 3
|
||||
title: Optimize cost at scale
|
||||
icon:
|
||||
src: /icons/outline/circle-dollar-sign.svg
|
||||
alt: Dollar
|
||||
|
||||
#testimonials-section
|
||||
- type: testimonials
|
||||
testimonials:
|
||||
- id: 0
|
||||
reverse: false
|
||||
review: “Qdrant cut retrieval time by 90%. That made it possible to stay under our latency SLA.”
|
||||
author:
|
||||
name: Kshitiz Parashar
|
||||
role: AI Engineer, Alhena
|
||||
avatar:
|
||||
src: /img/e-commerce/customer1.svg
|
||||
alt: Kshitiz Parashar avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 90%
|
||||
description: Latency Improvement
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/chart-no-axes-combined-green.svg
|
||||
alt: Chart
|
||||
description: Scaled Multitenancy
|
||||
logo:
|
||||
src: /img/e-commerce/alhena.svg
|
||||
alt: Alhena logo
|
||||
- id: 1
|
||||
reverse: true
|
||||
review: “Vector search is a key for modern AI infrastructure. Not just for fraud detection, but as a foundation for new AI systems.”
|
||||
author:
|
||||
name: Shardul Aggarwal
|
||||
role: SDE-III, Trust & Safety, Flipkart
|
||||
avatar:
|
||||
src: /img/e-commerce/customer2.svg
|
||||
alt: Shardul Aggarwal avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 99%+
|
||||
description: Reduction in fraud detection time
|
||||
logo:
|
||||
src: /img/e-commerce/flipkart.svg
|
||||
alt: Aracor logo
|
||||
|
||||
#bento-cards-section
|
||||
- type: bento-cards
|
||||
subtitle: Why Teams Choose Qdrant
|
||||
title: Semantic and Multimodal Need Native Vector Search
|
||||
description: Many teams that come to us are already running vector search. But they hit a wall at filter performance, cost, or scale. Here's what they’re saying.
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/gauge-orange.svg
|
||||
alt: Gauge
|
||||
title: Search Latency Kills Conversion
|
||||
description: Traditional solutions show 150-200ms+ latency for vector search. Every 100ms delay costs measurable revenue. Qdrant can deliver sub-50ms P95 with hybrid search at 1000+ QPS.
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/circle-alert.svg
|
||||
alt: Circle alert
|
||||
title: Keyword Search Fails Your Shoppers
|
||||
description: '"Blue T-shirt with yellow buttons" returns nothing. "Evening dress accessories" gets zero results. Qdrant''s hybrid search combines semantic understanding with keyword matching and metadata filters to eliminate zero-result pages.'
|
||||
|
||||
#case-studies-section
|
||||
- type: case-studies
|
||||
title: Here’s Why Clients Migrate to Qdrant
|
||||
caseStudies:
|
||||
- id: 0
|
||||
title: “We used Postgres to ship. It was a short-term answer.”
|
||||
description: Postgres is fast to start, but can’t scale. Users deal with manual partitioning, latency spikes, and climbing storage. Qdrant is proven at scale.
|
||||
- id: 1
|
||||
title: “Our search latency is unpredictable”
|
||||
description: Java-based solutions have 200ms+ latency for vector search, directly costing revenue. Qdrant delivers faster, more predictable latency.
|
||||
- id: 2
|
||||
title: “Filters destroy our recall.”
|
||||
description: Pre-filtering and Post-filter both have tradeoffs. Qdrant’s one-stage filtering eliminates this dilemma.
|
||||
|
||||
#get-contacted-section
|
||||
- type: get-contacted
|
||||
title: Evaluating Migration?
|
||||
description: Our solutions engineers do technical deep-dives with E-commerce search teams.
|
||||
contactUs:
|
||||
text: Book a Session
|
||||
url: /contact-us/
|
||||
|
||||
#bento-cards-section
|
||||
- type: bento-cards
|
||||
title: What you can build with Qdrant
|
||||
description: From product discovery to fraud detection, e-commerce teams combine Qdrant's retrieval primitives to solve problems generic search engines can't.
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/search-blue.svg
|
||||
alt: Search
|
||||
title: Product Search & Discovery
|
||||
description: '"Blue T-shirt with yellow buttons" returns relevant results instead of zero matches. Dense vector similarity understands product intent.'
|
||||
chips:
|
||||
- id: 0
|
||||
title: Hybrid Search
|
||||
link: /documentation/search/hybrid-queries/
|
||||
- id: 1
|
||||
title: Payload Filters
|
||||
link: /documentation/search/filtering/
|
||||
- id: 2
|
||||
title: RRF Fusion
|
||||
link: /documentation/search/hybrid-queries/#reciprocal-rank-fusion-rrf
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/file-text-blue.svg
|
||||
alt: File text
|
||||
title: Personalized Recommendation
|
||||
description: 'Item-to-item similarity vectors surface "You might also like" recommendations with fast latency.'
|
||||
chips:
|
||||
- id: 0
|
||||
title: Recommendations API
|
||||
link: /documentation/search/explore/#recommendation-api
|
||||
- id: 1
|
||||
title: Filtering
|
||||
link: /documentation/search/filtering/
|
||||
- id: 2
|
||||
icon:
|
||||
src: /icons/outline/heart-handshake.svg
|
||||
alt: Handshake
|
||||
title: Inventory & Catalog Intelligence
|
||||
description: Find similar/duplicate listings across millions of SKUs using image and text embeddings. Flipkart uses this for fraud detection.
|
||||
chips:
|
||||
- id: 0
|
||||
title: Recommendation API
|
||||
link: /documentation/search/explore/#recommendation-api
|
||||
- id: 0
|
||||
title: Discovery API
|
||||
link: /documentation/search/explore/#discovery-api
|
||||
|
||||
#architecture-section
|
||||
- type: architecture
|
||||
title: Common E-commerce Patterns
|
||||
description: Architecture patterns with API examples for e-commerce search, recommendations, and multi-tenancy.
|
||||
sections:
|
||||
- id: 0
|
||||
title: Hybrid Product Search Pipeline
|
||||
description: Combine semantic understanding, keyword matching, and business rules in a single query.
|
||||
link:
|
||||
href: /articles/sparse-embeddings-ecommerce-part-1/
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Dense Vectors
|
||||
description: (e.g. OpenAI, Cohere) for semantic product understanding
|
||||
- id: 1
|
||||
title: Sparse Vectors
|
||||
description: (BM25/SPLADE) for exact keyword matching
|
||||
language: Python
|
||||
code: |
|
||||
# Hybrid search with business-logic reranking
|
||||
results = client.query_points(
|
||||
collection_name="products",
|
||||
prefetch=[
|
||||
Prefetch(query=dense_emb, using="dense", limit=100),
|
||||
Prefetch(query=sparse_emb, using="sparse", limit=100),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("in_stock", match=MatchValue(True)),
|
||||
FieldCondition("category", match=MatchValue("shoes")),
|
||||
FieldCondition("price", range=Range(lte=150.0)),
|
||||
]),
|
||||
limit=20,
|
||||
)
|
||||
- id: 1
|
||||
title: Multitenant Marketplace Architecture
|
||||
description: Isolate each seller's catalog within a single shared cluster.
|
||||
link:
|
||||
href: /documentation/manage-data/multitenancy/
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Per-tenant HNSW indexes
|
||||
description: Via payload indexing
|
||||
- id: 1
|
||||
title: Tenant Promotion
|
||||
description: Move large tenants to dedicated shards
|
||||
- id: 2
|
||||
title: Custom shard keys
|
||||
description: For geo or time partitioning
|
||||
language: Python
|
||||
code: |
|
||||
# Payload-based multi-tenancy
|
||||
client.create_collection(
|
||||
"marketplace",
|
||||
vectors_config=VectorParams(size=1536, distance="Cosine"),
|
||||
hnsw_config=HnswConfigDiff(payload_m=16, m=0),
|
||||
on_disk_payload=True,
|
||||
)
|
||||
|
||||
# Create per-tenant index
|
||||
client.create_payload_index(
|
||||
"marketplace", "tenant_id",
|
||||
field_schema=PayloadSchemaType.KEYWORD,
|
||||
is_tenant=True, # enables per-tenant HNSW
|
||||
)
|
||||
|
||||
# Query scoped to tenant
|
||||
client.query_points(
|
||||
"marketplace",
|
||||
query=embedding,
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("tenant_id", match=MatchValue("brand_123"))
|
||||
]),
|
||||
limit=20,
|
||||
)
|
||||
- id: 2
|
||||
title: Real-Time Recommendations Engine
|
||||
description: User interactions update vectors in real time. No batch processing delays. Combine item similarity, user profiles, and contextual signals with business rules for margins, inventory, and promotions.
|
||||
link:
|
||||
href: /documentation/search/explore/?q=recommendation#recommendation-api
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Item-to-Item
|
||||
description: Similarity via dense vectors
|
||||
- id: 1
|
||||
title: Business rules
|
||||
description: (Margin, inventory) via metadata filters
|
||||
- id: 2
|
||||
title: Recommend API
|
||||
description: For behavioral matching
|
||||
language: Python
|
||||
code: |
|
||||
# Real-time recommendation with business rules
|
||||
results = client.recommend(
|
||||
collection_name="products",
|
||||
positive=[last_viewed_id, last_purchased_id],
|
||||
negative=[returned_item_id],
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("in_stock", match=MatchValue(True)),
|
||||
FieldCondition("margin", range=Range(gte=0.25)),
|
||||
]),
|
||||
strategy=RecommendStrategy.BEST_SCORE,
|
||||
limit=12,
|
||||
)
|
||||
|
||||
# Update user vector on interaction (real-time)
|
||||
client.set_payload(
|
||||
"users",
|
||||
payload={"last_active": datetime.now().isoformat()},
|
||||
points=[user_id],
|
||||
)
|
||||
|
||||
#logos-section
|
||||
- type: logos
|
||||
title: Powering E-Commerce Applications For
|
||||
logos:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /img/e-commerce/flipkart-light.svg
|
||||
alt: Flipkart logo
|
||||
- id: 1
|
||||
icon:
|
||||
src: /img/e-commerce/alhena-light.svg
|
||||
alt: Alhena logo
|
||||
- id: 2
|
||||
icon:
|
||||
src: /img/e-commerce/meesho-light.svg
|
||||
alt: Meesho logo
|
||||
- id: 3
|
||||
icon:
|
||||
src: /img/e-commerce/convo-search-light.svg
|
||||
alt: ConvoSearch logo
|
||||
- id: 5
|
||||
icon:
|
||||
src: /img/e-commerce/bazaarvoice-light.svg
|
||||
alt: Bazaarvoice logo
|
||||
|
||||
#testimonials-section
|
||||
- type: testimonials
|
||||
testimonials:
|
||||
- id: 0
|
||||
reverse: true
|
||||
review: “Qdrant transformed our recommendation engine capabilities, making us indispensable to our clients.”
|
||||
author:
|
||||
name: Shardul Aggarwal
|
||||
role: CEO, ConvoSearch
|
||||
avatar:
|
||||
src: /img/e-commerce/customer3.svg
|
||||
alt: Shardul Aggarwal avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 50%+
|
||||
description: Latency Improvement from 100ms to 10ms
|
||||
- id: 1
|
||||
title: 60%
|
||||
description: Increase revenue for Convosearch clients
|
||||
logo:
|
||||
src: /img/e-commerce/convo-search.svg
|
||||
alt: ConvoSearch logo
|
||||
|
||||
#faq-section
|
||||
- type: faq
|
||||
title: FAQs
|
||||
questions:
|
||||
- id: 0
|
||||
question: Can Qdrant Handle Our Traffic Spikes During Sales Events?
|
||||
answer: Yes. Qdrant's horizontal scaling with auto-sharding handles 4x-100x traffic spikes without manual intervention. Add nodes and the operator auto-distributes shards. Quantization (scalar for 4x compression, binary for 32x) keeps memory costs predictable even at peak load.
|
||||
- id: 1
|
||||
question: What Deployment Options Work for Multi-Region E-Commerce?
|
||||
answer: Qdrant supports managed cloud, BYOC (any cloud with Kubernetes), hybrid cloud, on-prem, and edge deployments. SOC2 and GDPR compliant. EU-based company. Multi-AZ deployment with zero-downtime upgrades for 99.99% availability.
|
||||
- id: 2
|
||||
question: How Does Multi-Tenancy Work for Marketplace Platforms?
|
||||
answer: Qdrant supports payload-based multi-tenancy that scales to 100k+ tenants in a single collection. Per-tenant HNSW indexes with disabled global indexing prevent cross-tenant interference. Large tenants can be promoted to dedicated shards. This avoids the file descriptor limits of collection-per-tenant approaches.
|
||||
- id: 3
|
||||
question: Can We Combine Image and Text Search in One Query?
|
||||
answer: Yes. Qdrant's multi-vector support lets you store and search across dense embeddings (semantic), sparse embeddings (keyword), image embeddings (CLIP/ColPali), and user behavior embeddings simultaneously. Prefetching enables parallel multi-modal retrieval with RRF fusion for balanced ranking.
|
||||
- id: 4
|
||||
question: How Does Qdrant Compare to Legacy search engines for E-Commerce Search?
|
||||
answer: Legacy Java-based search engines were built for text search and added vector capabilities as a bolt-on. Qdrant is purpose-built for vector workloads with native hybrid search (dense + sparse vectors + metadata filters in a single query). E-commerce teams report significant improvements when migrating.
|
||||
|
||||
#cta-banner-section
|
||||
- type: cta-banner
|
||||
title: Talk to an expert about <br><span>E-commerce</span> retrieval.
|
||||
description: Let’s discuss your catalog size, traffic patterns, and current stack.
|
||||
button:
|
||||
text: Talk to an Expert
|
||||
url: /contact-us/
|
||||
|
||||
build:
|
||||
render: always
|
||||
cascade:
|
||||
- build:
|
||||
list: local
|
||||
publishResources: false
|
||||
render: never
|
||||
---
|
||||
@@ -0,0 +1,422 @@
|
||||
---
|
||||
title: Hospitality & Travel
|
||||
description: Discover how Qdrant's vector search technology can transform the hospitality and travel industry by enhancing personalization, improving content discovery, and optimizing fraud detection.
|
||||
url: /hospitality-and-travel/
|
||||
social_preview_image: /hospitality-and-travel/social-preview.png
|
||||
sections:
|
||||
#hero-section
|
||||
- type: hero
|
||||
badge:
|
||||
title: Travel & Hospitality
|
||||
icon:
|
||||
src: /icons/outline/briefcase-business.svg
|
||||
alt: Scales
|
||||
title: Deliver fast and accurate semantic search
|
||||
description: '"Hip modern bar near the beach" doesn’t work with keyword search. Qdrant enables semantic understanding, real-time availability, and hybrid search that’s predictably fast.'
|
||||
containedButton:
|
||||
text: Talk to an Expert
|
||||
url: /contact-us/
|
||||
outlinedButton:
|
||||
text: Read the Docs
|
||||
url: /documentation/
|
||||
language: Python
|
||||
code: |
|
||||
# Hybrid venue search with availability
|
||||
results = client.query_points(
|
||||
collection_name="venues",
|
||||
prefetch=[
|
||||
Prefetch(query=dense_emb, using="dense", limit=100),
|
||||
Prefetch(query=sparse_emb, using="sparse", limit=100),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("available", match=MatchValue(True)),
|
||||
FieldCondition("city", match=MatchValue("Paris")),
|
||||
FieldCondition("rating", range=Range(gte=4.0)),
|
||||
FieldCondition("price_per_night",
|
||||
range=Range(lte=300.0)),
|
||||
]),
|
||||
limit=20,
|
||||
)
|
||||
steps:
|
||||
- id: 0
|
||||
title: Step 1
|
||||
description: Embed - Parse + Embed Document
|
||||
icon:
|
||||
src: /icons/outline/square-code.svg
|
||||
alt: Code
|
||||
- id: 1
|
||||
title: Step 2
|
||||
description: Search - Semantic Search + Strict Filter
|
||||
icon:
|
||||
src: /icons/outline/filter-blue-small.svg
|
||||
alt: Filter
|
||||
- id: 2
|
||||
title: Step 3
|
||||
description: Rank - Rank + Rerank (Optional)
|
||||
icon:
|
||||
src: /icons/outline/list.svg
|
||||
alt: List
|
||||
- id: 3
|
||||
title: Step 4
|
||||
description: Result - Evidence-based Match
|
||||
icon:
|
||||
src: /icons/outline/circle-check.svg
|
||||
alt: Check
|
||||
badges:
|
||||
- id: 0
|
||||
title: Ultra-low latency
|
||||
icon:
|
||||
src: /icons/outline/filter-green.svg
|
||||
alt: Filter
|
||||
- id: 1
|
||||
title: Hybrid Search
|
||||
icon:
|
||||
src: /icons/outline/rocket-blue-small.svg
|
||||
alt: Rocket
|
||||
- id: 2
|
||||
title: Billion+ Vector Scale
|
||||
icon:
|
||||
src: /icons/outline/circle-dollar-sign.svg
|
||||
alt: Dollar
|
||||
- id: 3
|
||||
title: Native inference capability
|
||||
icon:
|
||||
src: /icons/outline/server.svg
|
||||
alt: Server
|
||||
|
||||
#testimonials-section
|
||||
- type: testimonials
|
||||
testimonials:
|
||||
- id: 0
|
||||
reverse: false
|
||||
review: “With a billion+ user-generated, multimodal reviews from 100s of millions of MAUs, you need a way to bring it together.”
|
||||
author:
|
||||
name: Rahul Todkar
|
||||
role: Head of Data and AI, Tripadvisor
|
||||
avatar:
|
||||
src: /img/hospitality-and-travel/customer1.svg
|
||||
alt: Rahul Todkar avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 2-3x
|
||||
description: Revenue
|
||||
- id: 1
|
||||
title: 1B+
|
||||
description: Reviews Indexed
|
||||
logo:
|
||||
src: /img/hospitality-and-travel/tripadvisor.svg
|
||||
alt: Tripadvisor logo
|
||||
- id: 1
|
||||
reverse: true
|
||||
review: “Since running it in production, it's probably been one of the most frictionless parts of the stack.”
|
||||
author:
|
||||
name: Patrick Lombardo
|
||||
role: Staff ML Engineer, Opentable
|
||||
avatar:
|
||||
src: /img/hospitality-and-travel/customer2.svg
|
||||
alt: Patrick Lombardo avatar
|
||||
metric:
|
||||
- id: 0
|
||||
description: Fast, Predictable Response Latency
|
||||
- id: 1
|
||||
title: ">60K"
|
||||
description: Searched with Precision Filtering
|
||||
logo:
|
||||
src: /img/hospitality-and-travel/open-table.svg
|
||||
alt: OpenTable logo
|
||||
|
||||
#bento-cards-section
|
||||
- type: bento-cards
|
||||
subtitle: Why Teams Choose Qdrant
|
||||
title: Legacy Search Engines Weren't Built for How Travelers Think
|
||||
description: Keyword search, rigid filters, and batch pipelines break under the demands of modern travel platforms. Teams running semantic search, AI assistants, and real-time inventory hit the same walls.
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/gauge.svg
|
||||
alt: Gauge
|
||||
title: Real-Time Updates Spike Your Latency
|
||||
description: Batch inventory updates can spike P95 latency. Availability changes for hotels and restaurants can't wait for reindexing.<br><br>Qdrant's real-time payload updates change availability, pricing, and inventory without touching the index.
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/filter-pink.svg
|
||||
alt: Filter
|
||||
title: Structured Filters Can't Capture Intent
|
||||
description: Travelers search semantically (e.g. "romantic rooftop dinner.") Even 600+ filterable attributes can still miss unstructured intent.<br><br>Qdrant's hybrid search combines semantic understanding with structured filters for amenities, location, and availability.
|
||||
|
||||
#case-studies-section
|
||||
- type: case-studies
|
||||
title: Why People Migrate to Qdrant
|
||||
caseStudies:
|
||||
- id: 0
|
||||
title: “We couldn’t provide high quality, fast retrieval ”
|
||||
description: Write performance with legacy, Java-based search engines couldn’t keep up.
|
||||
- id: 1
|
||||
title: “Vector search must adapt to different workloads”
|
||||
description: Fine-tuning configurations at the collection level is crucial for varied AI applications
|
||||
- id: 2
|
||||
title: “RAM Usage Spiked Costs”
|
||||
description: Quantization and memory mapping enables customers to reduce RAM usage, meeting cost goals
|
||||
|
||||
#get-contacted-section
|
||||
- type: get-contacted
|
||||
title: Evaluating Migration?
|
||||
description: Our solutions engineers do technical deep-dives with Travel and Hospitality tech teams weekly.
|
||||
contactUs:
|
||||
text: Book a Session
|
||||
url: /contact-us/
|
||||
|
||||
#bento-cards-section
|
||||
- type: bento-cards
|
||||
title: What you can build with Qdrant
|
||||
description: From AI concierges to semantic property search, travel and hospitality teams combine Qdrant's retrieval primitives to deliver experiences keyword search never could.
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/search-blue.svg
|
||||
alt: Search
|
||||
title: AI-Powered Discovery
|
||||
description: '"Find me a romantic rooftop restaurant with a view in Rome" returns ranked results that understand ambiance, cuisine, and location intent. RAG over reviews grounds AI responses in real guest experiences.'
|
||||
chips:
|
||||
- id: 0
|
||||
title: Hybrid Search
|
||||
link: /documentation/search/hybrid-queries/
|
||||
- id: 1
|
||||
title: Payload Filters
|
||||
link: /documentation/search/filtering/
|
||||
- id: 2
|
||||
title: Reciprocal Rank Fusion (RRF)
|
||||
link: /documentation/search/hybrid-queries/#reciprocal-rank-fusion-rrf
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/file-text-blue.svg
|
||||
alt: File
|
||||
title: Semantic Venue Search
|
||||
description: "\"Competition lap pool\" or \"hip modern bar\" aren't filterable fields. Dense embeddings over reviews and descriptions surface properties matching intent."
|
||||
chips:
|
||||
- id: 0
|
||||
title: Semantic-Search
|
||||
link: /documentation/search/text-search/#semantic-search
|
||||
- id: 2
|
||||
icon:
|
||||
src: /icons/outline/heart-handshake.svg
|
||||
alt: Handshake
|
||||
title: Personalized Recommendations
|
||||
description: Capture dining style, travel patterns, and review history. Surface personalized recommendations, updated with every interaction.
|
||||
chips:
|
||||
- id: 0
|
||||
title: Recommend API
|
||||
link: /documentation/search/explore/#recommendation-api
|
||||
- id: 1
|
||||
title: Discovery API
|
||||
link: /documentation/search/explore/#discovery-api
|
||||
|
||||
#architecture-section
|
||||
- type: architecture
|
||||
title: How It Works Under the Hood
|
||||
description: Architecture patterns with API examples for travel and hospitality search, recommendations, and multi-tenancy.
|
||||
sections:
|
||||
- id: 0
|
||||
title: Hybrid Search
|
||||
description: Guest intent is part structured (dates, location, price) and part unstructured ("romantic," "family-friendly," "hip vibe"). This pattern fuses semantic and keyword retrieval with structured inventory filters in a single query.
|
||||
link:
|
||||
href: /documentation/search/hybrid-queries/?q=hybrid#hybrid-search
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Dense Vectors
|
||||
description: Semantic understanding of reviews and descriptions
|
||||
- id: 1
|
||||
title: Sparse Vectors (BM25/SPLADE)
|
||||
description: For exact terms (e.g. cuisine, chain, amenity names)
|
||||
language: Python
|
||||
code: |
|
||||
# Hybrid venue search with availability
|
||||
results = client.query_points(
|
||||
collection_name="venues",
|
||||
prefetch=[
|
||||
Prefetch(query=dense_emb, using="dense", limit=100),
|
||||
Prefetch(query=sparse_emb, using="sparse", limit=100),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("available", match=MatchValue(True)),
|
||||
FieldCondition("city", match=MatchValue("Paris")),
|
||||
FieldCondition("rating", range=Range(gte=4.0)),
|
||||
FieldCondition("price_per_night",
|
||||
range=Range(lte=300.0)),
|
||||
]),
|
||||
limit=20,
|
||||
)
|
||||
- id: 1
|
||||
title: RAG-Powered AI Concierge
|
||||
description: "Ground AI assistants in real guest reviews and venue data. This is the pattern OpenTable uses for Concierge: vectorize review chunks, retrieve relevant context per question, and generate grounded answers that reflect actual guest experiences."
|
||||
link:
|
||||
href: /blog/case-study-opentable/
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Hybrid retrieval for reviews
|
||||
description: Dense + sparse captures vibe and specifics
|
||||
- id: 1
|
||||
title: Scoped filtering
|
||||
description: Retrieve only reviews for what’s being asked about
|
||||
- id: 2
|
||||
title: Real-time payload updates
|
||||
description: Quickly Index New Reviews to Avoid Stale Recommendations
|
||||
language: Python
|
||||
code: |
|
||||
# RAG: retrieve review context for AI assistant
|
||||
review_chunks = client.query_points(
|
||||
collection_name="review_chunks",
|
||||
prefetch=[
|
||||
Prefetch(query=question_emb, using="dense",
|
||||
limit=50),
|
||||
Prefetch(query=question_sparse, using="sparse",
|
||||
limit=50),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("venue_id",
|
||||
match=MatchValue("restaurant_456")),
|
||||
]),
|
||||
limit=10,
|
||||
)
|
||||
|
||||
# Feed to LLM with grounded context
|
||||
context = "\n".join([r.payload["text"]
|
||||
for r in review_chunks.points])
|
||||
answer = llm.generate(
|
||||
f"Based on reviews: {context}\n"
|
||||
f"Question: {user_question}"
|
||||
)
|
||||
- id: 2
|
||||
title: Geo-Partitioned Search
|
||||
description: Custom shard keys partition inventory by region so queries stay local, with cross-region discovery when users browse globally.
|
||||
link:
|
||||
href: /documentation/search/filtering/#geo
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Custom shard keys by region
|
||||
description: Geo-hash or country code — queries stay local to the shard
|
||||
- id: 1
|
||||
title: Cross-region discovery
|
||||
description: Search across shards when users browse globally
|
||||
- id: 2
|
||||
title: Time-based sharding for seasonal inventory
|
||||
description: Easy deletion of expired listings without reindexing
|
||||
language: Python
|
||||
code: |
|
||||
# Geo-partitioned collection
|
||||
client.create_collection(
|
||||
"global_venues",
|
||||
vectors_config=VectorParams(
|
||||
size=1536, distance="Cosine"),
|
||||
sharding_method=ShardingMethod.CUSTOM,
|
||||
)
|
||||
|
||||
# Create region shard
|
||||
client.create_shard_key(
|
||||
"global_venues",
|
||||
shard_key="europe_west",
|
||||
)
|
||||
|
||||
# Upsert with region routing
|
||||
client.upsert(
|
||||
"global_venues",
|
||||
points=[PointStruct(
|
||||
id=1,
|
||||
vector=venue_embedding,
|
||||
payload={
|
||||
"region": "europe_west",
|
||||
"city": "Barcelona",
|
||||
"type": "hotel",
|
||||
"available": True,
|
||||
},
|
||||
)],
|
||||
shard_key_selector="europe_west",
|
||||
)
|
||||
|
||||
# Query scoped to region
|
||||
client.query_points(
|
||||
"global_venues",
|
||||
query=embedding,
|
||||
shard_key_filter=["europe_west"],
|
||||
limit=20,
|
||||
)
|
||||
|
||||
#logos-section
|
||||
- type: logos
|
||||
title: Powering Search For
|
||||
logos:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /img/hospitality-and-travel/open-table-light.svg
|
||||
alt: OpenTable logo
|
||||
- id: 1
|
||||
icon:
|
||||
src: /img/hospitality-and-travel/tripadvisor-light.svg
|
||||
alt: Tripadvisor logo
|
||||
- id: 2
|
||||
icon:
|
||||
src: /img/hospitality-and-travel/sprinklr-light.svg
|
||||
alt: Sprinklr logo
|
||||
|
||||
#testimonials-section
|
||||
- type: testimonials
|
||||
testimonials:
|
||||
- id: 1
|
||||
reverse: false
|
||||
review: “Qdrant not only delivered on our performance requirements, but also kept costs in check”
|
||||
author:
|
||||
name: Raghav Sonavane
|
||||
role: Associate Director ML, Sprinklr
|
||||
avatar:
|
||||
src: /img/hospitality-and-travel/customer3.svg
|
||||
alt: Raghav Sonavane avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 90%
|
||||
description: Faster Indexing Time
|
||||
- id: 1
|
||||
title: 30%
|
||||
description: Lower Retrieval Costs
|
||||
logo:
|
||||
src: /img/hospitality-and-travel/sprinklr.svg
|
||||
alt: Sprinklr logo
|
||||
|
||||
#faq-section
|
||||
- type: faq
|
||||
title: FAQs
|
||||
questions:
|
||||
- id: 0
|
||||
question: How Does Qdrant Handle Real-Time Availability Updates Without Latency Spikes?
|
||||
answer: Qdrant separates payload updates from vector indexing. When a hotel room sells out or a restaurant table fills, you update the availability payload field directly. This doesn't trigger reindexing, so your search latency stays flat. Teams processing millions of inventory changes per day use this to keep results accurate to the second without the P95 spikes batch reindexing causes.
|
||||
- id: 1
|
||||
question: Can Qdrant Scale to Billions of Review Embeddings?
|
||||
answer: Yes. Travel platforms generate massive embedding volumes from reviews, listings, and images. Qdrant's quantization options (scalar for 4× compression, binary for 32×) and disk offloading keep this manageable at scale. Tripadvisor, for example, uses Qdrant to unlock insights from over a billion user-generated contributions and hundreds of millions of images across its AI-powered travel platform.
|
||||
- id: 2
|
||||
question: How Does Hybrid Search Work for Travel Discovery?
|
||||
answer: Travel search is part structured (dates, location, price, rating) and part unstructured ("hip modern bar" or "romantic rooftop dinner"). Qdrant's hybrid search fuses dense vectors (semantic understanding from reviews and descriptions), sparse vectors (BM25 keyword matching for exact terms), and metadata filters (location, availability, price) in a single query using RRF fusion.
|
||||
- id: 3
|
||||
question: What Deployment Options Are There?
|
||||
answer: Qdrant supports managed cloud, BYOC (any cloud with Kubernetes), hybrid cloud, on-prem, and edge. Custom shard keys enable geo-partitioned architectures where queries stay region-local for low latency. Multi-AZ deployment on Premium tier provides a 99.9% uptime SLA. Qdrant is SOC2 and GDPR compliant, and an EU-based company.
|
||||
|
||||
#cta-banner-section
|
||||
- type: cta-banner
|
||||
title: Talk to an expert about <span>Travel and Hospitality</span> retrieval.
|
||||
description: We'll show you the architecture that fits.
|
||||
button:
|
||||
text: Talk to an Expert
|
||||
url: /contact-us/
|
||||
|
||||
build:
|
||||
render: always
|
||||
cascade:
|
||||
- build:
|
||||
list: local
|
||||
publishResources: false
|
||||
render: never
|
||||
---
|
||||
+2
-1
@@ -1,6 +1,7 @@
|
||||
---
|
||||
title: HR Tech & Talent Marketplaces
|
||||
description: Discover how Qdrant's vector search technology can transform HR marketplaces and recruitment platforms by powering intelligent job matching, resume search, and candidate recommendations.
|
||||
url: /hr-tech/
|
||||
sections:
|
||||
#hero-section
|
||||
- type: hero
|
||||
@@ -46,7 +47,7 @@ sections:
|
||||
steps:
|
||||
- id: 0
|
||||
title: Step 1
|
||||
description: Embed - Parse + Embed Resume / JD
|
||||
description: Embed - Parse + Embed Document
|
||||
icon:
|
||||
src: /icons/outline/square-code.svg
|
||||
alt: Code
|
||||
@@ -0,0 +1,450 @@
|
||||
---
|
||||
title: Legal tech
|
||||
description: Legal tech
|
||||
url: /legal-tech/
|
||||
social_preview_image: /legal-tech/social-preview.png
|
||||
sections:
|
||||
#hero-section
|
||||
- type: hero
|
||||
badge:
|
||||
title: LegalTech
|
||||
icon:
|
||||
src: /icons/outline/scales-blue.svg
|
||||
alt: Scales
|
||||
title: When Accuracy is<br>Non-Negotiable
|
||||
description: Hybrid search with metadata filtering for jurisdiction, case type, and document category. Build retrieval for patent corpora, M&A due diligence, and more.
|
||||
containedButton:
|
||||
text: Talk to an Expert
|
||||
url: /contact-us/
|
||||
outlinedButton:
|
||||
text: Read the Docs
|
||||
url: /documentation/
|
||||
language: Python
|
||||
code: |
|
||||
# Hybrid legal document search
|
||||
results = client.query_points(
|
||||
collection_name="legal_docs",
|
||||
prefetch=[
|
||||
Prefetch(query=dense_emb, using="dense",
|
||||
limit=100),
|
||||
Prefetch(query=sparse_emb, using="sparse",
|
||||
limit=100),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("jurisdiction",
|
||||
match=MatchValue("california")),
|
||||
FieldCondition("case_type",
|
||||
match=MatchValue("patent")),
|
||||
FieldCondition("filing_date",
|
||||
range=Range(gte="2020-01-01")),
|
||||
FieldCondition("settlement_amount",
|
||||
range=Range(gte=50000)),
|
||||
]),
|
||||
limit=20,
|
||||
)
|
||||
steps:
|
||||
- id: 0
|
||||
title: Step 1
|
||||
description: Embed - Parse + Embed Document
|
||||
icon:
|
||||
src: /icons/outline/square-code.svg
|
||||
alt: Code
|
||||
- id: 1
|
||||
title: Step 2
|
||||
description: Search - Semantic Search + Strict Filter
|
||||
icon:
|
||||
src: /icons/outline/filter-blue-small.svg
|
||||
alt: Filter
|
||||
- id: 2
|
||||
title: Step 3
|
||||
description: Rank - Rank + Rerank (Optional)
|
||||
icon:
|
||||
src: /icons/outline/list.svg
|
||||
alt: List
|
||||
- id: 3
|
||||
title: Step 4
|
||||
description: Result - Evidence-based Match
|
||||
icon:
|
||||
src: /icons/outline/circle-check.svg
|
||||
alt: Check
|
||||
badges:
|
||||
- id: 0
|
||||
title: Low latency
|
||||
icon:
|
||||
src: /icons/outline/filter-green.svg
|
||||
alt: Filter
|
||||
- id: 1
|
||||
title: High Accuracy
|
||||
icon:
|
||||
src: /icons/outline/target-blue.svg
|
||||
alt: Target
|
||||
- id: 2
|
||||
title: Billion+ Vector Scale
|
||||
icon:
|
||||
src: /icons/outline/circle-dollar-sign.svg
|
||||
alt: Dollar
|
||||
- id: 3
|
||||
title: GDPR/SOC2 Compliant
|
||||
icon:
|
||||
src: /icons/outline/shield-check.svg
|
||||
alt: Check
|
||||
- id: 4
|
||||
title: Hybrid Cloud
|
||||
icon:
|
||||
src: /icons/outline/cloud-blue.svg
|
||||
alt: Cloud
|
||||
|
||||
#testimonials-section
|
||||
- type: testimonials
|
||||
testimonials:
|
||||
- id: 0
|
||||
reverse: true
|
||||
review: “We scaled to a billion vectors with sub-second latency. Workflows that took hours now take minutes.”
|
||||
author:
|
||||
name: Herbie Turner
|
||||
role: CTO / Co-founder
|
||||
avatar:
|
||||
src: /img/legal-tech/customer1.svg
|
||||
alt: Herbie Turner avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 1B+
|
||||
description: Vectors in Production
|
||||
- id: 1
|
||||
title: 250B+
|
||||
description: Tokens Processed
|
||||
logo:
|
||||
src: /img/legal-tech/ai.svg
|
||||
alt: AI logo
|
||||
- id: 1
|
||||
reverse: false
|
||||
review: “We ingest thousands of legal docs, and need precise retrieval and accurate citations. Qdrant makes this possible.”
|
||||
author:
|
||||
name: Lesly Arun Franco
|
||||
role: CTO
|
||||
avatar:
|
||||
src: /img/legal-tech/customer2.svg
|
||||
alt: Lesly Arun Franco avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 90%
|
||||
description: Faster Due Diligence
|
||||
- id: 1
|
||||
title: 40%
|
||||
description: Fewer Legal Hours
|
||||
logo:
|
||||
src: /img/legal-tech/aracor.svg
|
||||
alt: Aracor logo
|
||||
|
||||
#bento-cards-section
|
||||
- type: bento-cards
|
||||
subtitle: Why Teams Choose Qdrant
|
||||
title: General-Purpose Databases Weren't Built for Legal AI
|
||||
description: Keyword search, rigid filters, and bolt-on vector capabilities break under the demands of modern legal platforms. Teams running semantic search, document analysis, and AI agents hit the same walls.
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/circle-alert.svg
|
||||
alt: Alert
|
||||
title: Legacy Search Can't Handle Legal Metadata
|
||||
description: "Legal documents can carry 2,000+ metadata fields: jurisdictions, case types, filing dates, settlement amounts. Other search engines require brute-force vector search with embedding constraints. Post-filter architectures degrade recall as filters multiply.<br><br>Qdrant's filterable HNSW applies filters during graph traversal."
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/file-check.svg
|
||||
alt: File check
|
||||
title: Compliance Blocks Most Cloud Providers
|
||||
description: Attorney-client privilege and data residency requirements mean managed cloud solutions are often non-starters. 100% of LegalTech sales conversations surface security and compliance.<br><br>Qdrant deploys on-prem, in private VPCs, or hybrid cloud — with SOC2, GDPR, and HIPAA-ready compliance built in.
|
||||
|
||||
#case-studies-section
|
||||
- type: case-studies
|
||||
title: Why People Migrate to Qdrant
|
||||
caseStudies:
|
||||
- id: 0
|
||||
title: “We outgrew keyword-based systems”
|
||||
description: Write performance with legacy, Java-based search engines couldn’t keep up.
|
||||
- id: 1
|
||||
title: “We needed rich filtering for legal documents”
|
||||
description: Fine-tuning configurations at the collection level was crucial for varied AI applications
|
||||
- id: 2
|
||||
title: “Data Sovereignty was critical”
|
||||
description: Qdrant offers hybrid cloud and private cloud deployments.
|
||||
- id: 3
|
||||
title: “Legal Corpora are massive and high stakes”
|
||||
description: Performance failures in legal AI have financial and reputational consequences<br><br>Qdrant's stays fast and accurate.
|
||||
|
||||
#get-contacted-section
|
||||
- type: get-contacted
|
||||
title: Evaluating Migration?
|
||||
description: Our solutions engineers do technical deep-dives with HR tech teams weekly.
|
||||
contactUs:
|
||||
text: Book a Session
|
||||
url: /contact-us/
|
||||
|
||||
#bento-cards-section
|
||||
- type: bento-cards
|
||||
title: What you can build with Qdrant
|
||||
description: From patent analysis to M&A due diligence, legal teams combine Qdrant's retrieval primitives to deliver citation-grade accuracy keyword search never could.
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/search-blue.svg
|
||||
alt: Search
|
||||
title: Jurisdiction-Scoped Document Search
|
||||
description: '"Find California patent cases from 2020 with settlements over $50k" returns ranked results filtered by jurisdiction, date, case type, and amount. Hybrid search grounds results in real documents.'
|
||||
chips:
|
||||
- id: 0
|
||||
title: Hybrid Search
|
||||
link: /documentation/search/hybrid-queries/
|
||||
- id: 1
|
||||
title: Payload Filters
|
||||
link: /documentation/search/filtering/
|
||||
- id: 2
|
||||
title: Reciprocal Rank Fusion (RRF)
|
||||
link: /documentation/search/hybrid-queries/#reciprocal-rank-fusion-rrf
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/file-badge-blue.svg
|
||||
alt: File badge
|
||||
title: Patent Prior Art / Claim Charting
|
||||
description: Search 200M+ patents by technology class, grant date, and patent family. Scalar quantization at billion-scale. Recommend API for claim-chart matching against prior art.
|
||||
chips:
|
||||
- id: 0
|
||||
title: Quantization
|
||||
link: /documentation/manage-data/quantization/
|
||||
- id: 2
|
||||
icon:
|
||||
src: /icons/outline/search-check-blue.svg
|
||||
alt: Search check
|
||||
title: M&A Due Diligence
|
||||
description: Automated signature validation, contract comparison, and risk flagging across thousands of deal documents. Multitenant isolation per deal room with real-time ingestion during active deals.
|
||||
chips:
|
||||
- id: 0
|
||||
title: Multitenancy
|
||||
link: /documentation/manage-data/multitenancy/
|
||||
|
||||
#architecture-section
|
||||
- type: architecture
|
||||
title: How It Works Under the Hood
|
||||
description: Architecture patterns with API examples for<br>legal document search, patent analysis, and multi-tenancy.
|
||||
sections:
|
||||
- id: 0
|
||||
title: Hybrid Search
|
||||
description: Legal queries are part structured (jurisdiction, date, case type) and part unstructured (“negligence in product liability” or “prior art for CRISPR gene editing”). This pattern fuses semantic and keyword retrieval with metadata filters in a single query.
|
||||
link:
|
||||
href: /documentation/search/hybrid-queries/
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Dense Vectors
|
||||
description: Semantic understanding of legal language and concepts
|
||||
- id: 1
|
||||
title: Sparse Vectors (BM25/SPLADE)
|
||||
description: For exact legal terms (e.g. statute numbers, case citations)
|
||||
language: Python
|
||||
code: |
|
||||
# Hybrid legal document search
|
||||
results = client.query_points(
|
||||
collection_name="legal_docs",
|
||||
prefetch=[
|
||||
Prefetch(query=dense_emb, using="dense",
|
||||
limit=100),
|
||||
Prefetch(query=sparse_emb, using="sparse",
|
||||
limit=100),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition(key="jurisdiction",
|
||||
match=MatchValue("california")),
|
||||
FieldCondition("case_type",
|
||||
match=MatchValue("patent")),
|
||||
]),
|
||||
limit=20,
|
||||
)
|
||||
- id: 1
|
||||
title: RAG-Powered Legal Research
|
||||
description: Ground AI assistants in real case law and statutes. This is the pattern Lawme uses for automated legal document drafting — vectorize document chunks, retrieve relevant context per question, and generate cited answers.
|
||||
link:
|
||||
href: /rag/
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Custom shard keys by jurisdiction
|
||||
description: US, AU, UK case law isolated
|
||||
- id: 1
|
||||
title: Scalar, binary, asymmetric quantization (8-bit)
|
||||
description: Up to 32x compression with reranking
|
||||
- id: 2
|
||||
title: Real-time payload updates
|
||||
description: Mark documents as reviewed without reindexing
|
||||
language: Python
|
||||
code: |
|
||||
# RAG: retrieve legal context for AI assistant
|
||||
review_chunks = client.query_points(
|
||||
collection_name="case_law",
|
||||
prefetch=[
|
||||
Prefetch(query=question_emb, using="dense",
|
||||
limit=50),
|
||||
Prefetch(query=question_sparse, using="sparse",
|
||||
limit=50),
|
||||
],
|
||||
query=FusionQuery(fusion=Fusion.RRF),
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("jurisdiction",
|
||||
match=MatchValue("australia")),
|
||||
]),
|
||||
limit=10,
|
||||
)
|
||||
|
||||
# Feed to LLM with grounded context
|
||||
context = "\n".join([r.payload["text"]
|
||||
for r in review_chunks.points])
|
||||
answer = llm.generate(
|
||||
f"Based on case law: {context}\n"
|
||||
f"Question: {user_question}"
|
||||
)
|
||||
- id: 2
|
||||
title: Large Patent Corpus Search
|
||||
description: Search hundreds of millions of patents across jurisdictions with scalar, binary or asymmetric quantization.
|
||||
link:
|
||||
href: /documentation/manage-data/quantization/
|
||||
text: View Full Example
|
||||
steps:
|
||||
- id: 0
|
||||
title: Scalar, binary, asymmetric quantization (8-bit)
|
||||
description: Keeps hot vectors in RAM, full-precision on disk
|
||||
- id: 1
|
||||
title: Recommend API for claim charting
|
||||
description: Find prior art similar to specific patent claims
|
||||
- id: 2
|
||||
title: Pay-for-what-you-use pricing
|
||||
description: 10× more data for the same cost
|
||||
language: Python
|
||||
code: |
|
||||
# Prior art search at billion scale
|
||||
results = client.query_points(
|
||||
collection_name="patents",
|
||||
query=claim_embedding,
|
||||
query_filter=Filter(must=[
|
||||
FieldCondition("grant_date",
|
||||
range=Range(lte="2015-06-01")),
|
||||
FieldCondition("technology_class",
|
||||
match=MatchAny(["H04L", "G06F"])),
|
||||
]),
|
||||
params=SearchParams(
|
||||
quantization=QuantizationSearchParams(
|
||||
rescore=True,
|
||||
oversampling=2.0
|
||||
)
|
||||
),
|
||||
limit=50,
|
||||
)
|
||||
|
||||
# Claim-chart matching via Recommend API
|
||||
similar = client.query_points(
|
||||
collection_name="patents",
|
||||
query=RecommendQuery(
|
||||
recommend=RecommendInput(
|
||||
positive=[claim_vector_id],
|
||||
negative=[known_irrelevant_id],
|
||||
)
|
||||
),
|
||||
limit=25,
|
||||
)
|
||||
|
||||
#logos-section
|
||||
- type: logos
|
||||
title: Powering Search For
|
||||
logos:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /img/legal-tech/ai-light.svg
|
||||
alt: AI logo
|
||||
- id: 1
|
||||
icon:
|
||||
src: /img/legal-tech/aracor-light.svg
|
||||
alt: Aracor logo
|
||||
- id: 2
|
||||
icon:
|
||||
src: /img/legal-tech/garden-light.svg
|
||||
alt: Garden logo
|
||||
- id: 3
|
||||
icon:
|
||||
src: /img/legal-tech/lawme-ai-light.svg
|
||||
alt: Lawme AI logo
|
||||
|
||||
#testimonials-section
|
||||
- type: testimonials
|
||||
testimonials:
|
||||
- id: 0
|
||||
reverse: true
|
||||
review: “To scale, you need vector search with low latency, high accuracy, and reasonable costs. Qdrant makes that possible.”
|
||||
author:
|
||||
name: Jordan Parker
|
||||
role: Co-founder Lawme
|
||||
avatar:
|
||||
src: /img/legal-tech/customer3.svg
|
||||
alt: Jordan Parker avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 10x
|
||||
description: faster query throughput
|
||||
- id: 1
|
||||
title: 75%
|
||||
description: reduction in retrieval costs
|
||||
logo:
|
||||
src: /img/legal-tech/lawme-ai.svg
|
||||
alt: Lawme AI
|
||||
- id: 1
|
||||
reverse: false
|
||||
review: “Filterable HNSW was the deal-maker. We don't have to think about the vector layer anymore.”
|
||||
author:
|
||||
name: Justin Mack
|
||||
role: CTO / Co-founder, Garden AI
|
||||
avatar:
|
||||
src: /img/legal-tech/customer4.svg
|
||||
alt: Justin Mack avatar
|
||||
metric:
|
||||
- id: 0
|
||||
title: 10×
|
||||
description: Lower cost per stored GB
|
||||
- id: 1
|
||||
title: <100ms
|
||||
description: p95 query latency at 200M+ patents
|
||||
logo:
|
||||
src: /img/legal-tech/garden.svg
|
||||
alt: Garden logo
|
||||
|
||||
#faq-section
|
||||
- type: faq
|
||||
title: FAQs
|
||||
questions:
|
||||
- id: 0
|
||||
question: How Does Qdrant Handle Complex Legal Metadata Filtering?
|
||||
answer: Qdrant applies filters during HNSW graph traversal, not after retrieval. This means filtering by jurisdiction, case type, date range, or settlement amount doesn't degrade recall or spike latency.
|
||||
- id: 1
|
||||
question: How Does Hybrid Search Work for Legal Research?
|
||||
answer: Legal search is part structured (jurisdiction, filing dates, case type) and part unstructured ("negligence in product liability"). Qdrant's hybrid search fuses dense vectors (semantic understanding), sparse vectors (BM25 for exact legal citations), and metadata filters in a single query using Reciprocal Rank (RRF) fusion.
|
||||
- id: 2
|
||||
question: What Deployment Options Work for Law Firms With Data Residency Requirements?
|
||||
answer: Qdrant supports managed cloud, BYOC (any cloud with Kubernetes), hybrid cloud, on-prem, Edge, and fully air-gapped deployments. Qdrant is SOC2 and GDPR compliant, and is an EU-based company.
|
||||
- id: 3
|
||||
question: How Does Qdrant Compare to PGVector for Legal AI?
|
||||
answer: PGVector works for early prototypes but degrades as datasets grow across jurisdictions. Lawme saw a 75% cost reduction after migrating from PGVector, with significantly lower query latencies handling tens of millions of vectors. Qdrant's purpose-built HNSW and quantization give it sustained performance at scale.
|
||||
|
||||
#cta-banner-section
|
||||
- type: cta-banner
|
||||
title: Talk to an expert about <br><span>LegalTech</span> retrieval.
|
||||
description: We'll show you the architecture that fits.
|
||||
button:
|
||||
text: Talk to an Expert
|
||||
url: /contact-us/
|
||||
|
||||
build:
|
||||
render: always
|
||||
cascade:
|
||||
- build:
|
||||
list: local
|
||||
publishResources: false
|
||||
render: never
|
||||
---
|
||||
@@ -1,12 +0,0 @@
|
||||
---
|
||||
title: Legal tech
|
||||
description: Legal tech
|
||||
social_preview_image: /legal-tech/social-preview.png
|
||||
build:
|
||||
render: always
|
||||
cascade:
|
||||
- build:
|
||||
list: local
|
||||
publishResources: false
|
||||
render: never
|
||||
---
|
||||
@@ -1,41 +0,0 @@
|
||||
---
|
||||
title: "Vector Search and AI can solve many of the Legal industry's biggest challenges:"
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/scale-error-green.svg
|
||||
alt: Scale error
|
||||
title: Missed Anomalies, Costly Mistakes
|
||||
description: Inconsistencies in case law and contracts slip past traditional review.
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/warning-blue.svg
|
||||
alt: Warning
|
||||
title: Contract Landmines Everywhere
|
||||
description: Hidden discrepancies and compliance risks go unnoticed until it’s too late.
|
||||
- id: 2
|
||||
icon:
|
||||
src: /icons/outline/bottlenecks-purple.svg
|
||||
alt: Bottlenecks
|
||||
title: Research Bottlenecks
|
||||
description: Slow, manual document review wastes hours and delays decisions.
|
||||
- id: 3
|
||||
icon:
|
||||
src: /icons/outline/search-error-purple.svg
|
||||
alt: Search error
|
||||
title: Endless Scrolling, Missed Precedents
|
||||
description: Keyword-based search buries critical case law under irrelevant results.
|
||||
- id: 4
|
||||
icon:
|
||||
src: /icons/outline/confused-bot-green.svg
|
||||
alt: Confused bot
|
||||
title: Legal AI That Doesn’t Understand Law
|
||||
description: Outdated tools fail at semantic understanding, leading to incomplete insights.
|
||||
- id: 5
|
||||
icon:
|
||||
src: /icons/outline/unclear-blue.svg
|
||||
alt: Unclear
|
||||
title: Conflicting Jurisdictions, Unclear Precedents
|
||||
description: Legal rulings and regulations vary across regions, making it difficult to apply consistent case law.
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,23 +0,0 @@
|
||||
---
|
||||
title: Who We Help Build AI Applications
|
||||
cards:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /img/legal-tech-help/law.svg
|
||||
alt: Law
|
||||
title: Law Firms & Legal Advisors
|
||||
description: Speed up case research and discovery with AI-powered vector search.
|
||||
- id: 1
|
||||
icon:
|
||||
src: /img/legal-tech-help/corporate-teams.svg
|
||||
alt: Corporate teams
|
||||
title: Corporate Compliance Teams
|
||||
description: Automate contract management and regulatory checks with vector-based intelligence.
|
||||
- id: 2
|
||||
icon:
|
||||
src: /img/legal-tech-help/saas-providers.svg
|
||||
alt: SaaS Providers
|
||||
title: Legal Tech SaaS Providers
|
||||
description: Enhance document search and classification with vector AI.
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,8 +0,0 @@
|
||||
---
|
||||
content: Empower Your Legal Team with AI & Vector Search
|
||||
contactUs:
|
||||
text: Talk to Sales
|
||||
url: /contact-us/
|
||||
sitemapExclude: true
|
||||
---
|
||||
|
||||
@@ -1,10 +0,0 @@
|
||||
---
|
||||
title: Empower Your Legal Team with AI & Vector Search
|
||||
button:
|
||||
text: Get Started
|
||||
url: https://cloud.qdrant.io/signup
|
||||
image:
|
||||
src: /img/rocket.svg
|
||||
alt: Rocket
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,13 +0,0 @@
|
||||
---
|
||||
badge: View the guide
|
||||
badgeIcon: /img/legal-tech-help/guidebook.svg
|
||||
title: "LegalTech Builder's Guide: Navigating Strategic<br> Decisions with Vector Search"
|
||||
description: "Learn how to deliver precise results for high-stakes legal<br>applications. We cover HNSW, filtering, late-interaction<br>models, and more."
|
||||
buttonText: Download the Guide
|
||||
buttonLink: https://qdrant.to/legal-tech
|
||||
image:
|
||||
src: /img/legal-tech-help/legal-tech-guide.png
|
||||
src2x: /img/legal-tech-help/legal-tech-guide-2x.png
|
||||
alt: LegalTech Guide - Document search interface
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -1,20 +0,0 @@
|
||||
---
|
||||
tag:
|
||||
name: AI-Powered Legal Search
|
||||
icon:
|
||||
src: /icons/outline/legal-blue.svg
|
||||
alt: Legal
|
||||
title: "Qdrant's Vector Search: Using AI to Address Common Legal Industry Challenges"
|
||||
description: Revolutionize legal research, contract analysis, and compliance workflows with Qdrant’s high-performance <b>vector search</b>—making legal intelligence faster and more accurate.
|
||||
startFree:
|
||||
text: Get Started
|
||||
url: https://cloud.qdrant.io/signup
|
||||
contactUs:
|
||||
text: Talk to Sales
|
||||
url: /contact-us/
|
||||
image:
|
||||
src: /img/legal-tech-hero.png
|
||||
alt: graphic
|
||||
sitemapExclude: true
|
||||
---
|
||||
|
||||
@@ -1,32 +0,0 @@
|
||||
---
|
||||
title: Our Customers Words
|
||||
testimonials:
|
||||
- name: Jordan Parker
|
||||
position: Co-Founder of Lawme.ai
|
||||
review: “The more data you feed into the agent, the better it gets. But to truly scale, you need a vector database that maintains low latency, high accuracy, and keeps costs in check. Qdrant makes that possible.”
|
||||
avatar:
|
||||
src: /img/customers/jordan-parker.svg
|
||||
alt: Jordan Parker Avatar
|
||||
logo:
|
||||
src: /img/brands/lawme-ai.svg
|
||||
alt: Lawme.ai Logo
|
||||
- name: Justin Mack
|
||||
position: Co-founder, CTO
|
||||
review: “Our customers need to compare millions of possible patent–product pairings in seconds, not days - that means vector search that can handle huge data sets and surgical-grade HNSW filtering.”
|
||||
avatar:
|
||||
src: /img/customers/justin-mack.svg
|
||||
alt: Justin Mack Avatar
|
||||
logo:
|
||||
src: /img/brands/garden.svg
|
||||
alt: Garden Logo
|
||||
- name: Lesly Franco
|
||||
position: CTO, Aracor
|
||||
review: “Search is a massive problem. Our platform ingests thousands of legal documents, each requiring precise retrieval and accurate citations. Without Qdrant, delivering this level of performance and scale was nearly impossible.”
|
||||
avatar:
|
||||
src: /img/customers/lesly-franco.svg
|
||||
alt: Lesly Franco Avatar
|
||||
logo:
|
||||
src: /img/brands/aracor.svg
|
||||
alt: Aracor Logo
|
||||
---
|
||||
|
||||
@@ -1,28 +0,0 @@
|
||||
---
|
||||
title: Why Choose Qdrant
|
||||
items:
|
||||
- id: 0
|
||||
icon:
|
||||
src: /icons/outline/matching-pink.svg
|
||||
alt: Matching
|
||||
description: "<b>Unparalleled Accuracy:</b> Deep semantic search beyond keyword matching."
|
||||
- id: 1
|
||||
icon:
|
||||
src: /icons/outline/security-shield-blue.svg
|
||||
alt: Security
|
||||
description: "<b>Enterprise-Grade Security:</b> Meets legal compliance standards."
|
||||
- id: 2
|
||||
icon:
|
||||
src: /icons/outline/integration-purple.svg
|
||||
alt: iIntegration
|
||||
description: "<b>Seamless API Integration:</b> Works with existing case management and document systems."
|
||||
- id: 3
|
||||
icon:
|
||||
src: /icons/outline/cloud-connections-green.svg
|
||||
alt: Cloud connections
|
||||
description: "<b>Deploy your way:</b> Use our managed cloud, hybrid cloud or private cloud. We support AWS, Azure, and GCP."
|
||||
image:
|
||||
src: /img/legal-tech-why-qdrant.svg
|
||||
alt: Why qdrant
|
||||
sitemapExclude: true
|
||||
---
|
||||
@@ -136,6 +136,7 @@
|
||||
@import 'partials/industries/industries-testimonials';
|
||||
@import 'partials/industries/industries-architecture';
|
||||
@import 'partials/industries/industries-dark-banner';
|
||||
@import 'partials/industries/industries-logos';
|
||||
@import 'partials/vsd/vsd-speaker-lineup';
|
||||
@import 'partials/vsd/vsd-last-year';
|
||||
@import 'partials/vsd/vsd-sponsors';
|
||||
|
||||
+4
-3
@@ -18,7 +18,8 @@
|
||||
}
|
||||
|
||||
&__title {
|
||||
margin-bottom: 0;
|
||||
max-width: pxToRem(920);
|
||||
margin: 0 auto;
|
||||
text-align: center;
|
||||
font-size: $h4-font-size;
|
||||
line-height: $spacer * 3;
|
||||
@@ -26,8 +27,8 @@
|
||||
}
|
||||
|
||||
&__description {
|
||||
margin-top: pxToRem(30);
|
||||
margin-bottom: 0;
|
||||
max-width: pxToRem(980);
|
||||
margin: pxToRem(30) auto 0;
|
||||
text-align: center;
|
||||
color: $neutral-30;
|
||||
}
|
||||
|
||||
+5
-1
@@ -2,7 +2,7 @@
|
||||
@use '../../mixins/get-contacted' as get-contacted;
|
||||
|
||||
.industries-get-contacted {
|
||||
@include get-contacted.base($spacer * 4, pxToRem(30), $spacer * 7.5, $spacer * 2.5, pxToRem(620));
|
||||
@include get-contacted.base($spacer * 4, pxToRem(30), $spacer * 7.5, $spacer * 2.5, pxToRem(720));
|
||||
|
||||
h3 {
|
||||
margin-bottom: $spacer * 0.5;
|
||||
@@ -21,4 +21,8 @@
|
||||
font-size: $font-size-md;
|
||||
line-height: $spacer * 1.5;
|
||||
}
|
||||
|
||||
a {
|
||||
flex-shrink: 0;
|
||||
}
|
||||
}
|
||||
+128
@@ -0,0 +1,128 @@
|
||||
@use '../../helpers/functions' as *;
|
||||
@use '../../mixins/marquee' as marquee;
|
||||
|
||||
.industries-logos {
|
||||
padding-top: $spacer * 7.5;
|
||||
padding-bottom: $spacer * 7.5;
|
||||
background-color: $neutral-10;
|
||||
|
||||
&__title {
|
||||
margin-bottom: $spacer * 1.5;
|
||||
color: $neutral-98;
|
||||
font-size: $h5-font-size;
|
||||
line-height: pxToRem(42);
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
&__list {
|
||||
display: none;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
position: relative;
|
||||
|
||||
&-item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
width: pxToRem(240);
|
||||
height: $spacer * 5;
|
||||
border: pxToRem(1) solid $neutral-20;
|
||||
margin-left: pxToRem(-1);
|
||||
}
|
||||
|
||||
&:before,
|
||||
&:after {
|
||||
content: "";
|
||||
display: block;
|
||||
position: absolute;
|
||||
bottom: 0;
|
||||
width: $spacer * 10;
|
||||
height: 100%;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
&:before {
|
||||
left: 0;
|
||||
background: linear-gradient(90deg, $neutral-10 50%, rgba(9, 14, 26, 0.00) 100%);
|
||||
}
|
||||
|
||||
&:after {
|
||||
right: 0;
|
||||
background: linear-gradient(90deg, rgba(9, 14, 26, 0.00) 0%, $neutral-10 50%);
|
||||
}
|
||||
}
|
||||
|
||||
&__marquee {
|
||||
position: relative;
|
||||
@include marquee.base(80px, 240px, 4, 4, 0, transparent, false, 20s, none);
|
||||
|
||||
--marquee-items: 4;
|
||||
$leftKeyframe: marqueeLeft;
|
||||
$rightKeyframe: marqueeRight;
|
||||
|
||||
&__block {
|
||||
width: calc(240px * var(--marquee-items, 4));
|
||||
}
|
||||
|
||||
&__inner {
|
||||
width: calc(var(--marquee-items, 4) / var(--marquee-items, 4) * 2 * 100%);
|
||||
}
|
||||
|
||||
@keyframes #{$leftKeyframe} {
|
||||
100% {
|
||||
left: calc(var(--marquee-items, 4) / var(--marquee-items, 4) * 2 * 100% * 0.5 * -1);
|
||||
}
|
||||
}
|
||||
@keyframes #{$rightKeyframe} {
|
||||
0% {
|
||||
left: calc(var(--marquee-items, 4) / var(--marquee-items, 4) * 2 * 100% * 0.5 * -1);
|
||||
}
|
||||
}
|
||||
|
||||
&__item {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
width: pxToRem(240);
|
||||
height: $spacer * 5;
|
||||
border: pxToRem(1) solid $neutral-20;
|
||||
border-right: 0;
|
||||
}
|
||||
|
||||
&:before,
|
||||
&:after {
|
||||
content: "";
|
||||
display: block;
|
||||
position: absolute;
|
||||
bottom: 0;
|
||||
width: $spacer * 3;
|
||||
height: 100%;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
&:before {
|
||||
left: 0;
|
||||
background: linear-gradient(90deg, $neutral-10 50%, rgba(9, 14, 26, 0.00) 100%);
|
||||
}
|
||||
|
||||
&:after {
|
||||
right: 0;
|
||||
background: linear-gradient(90deg, rgba(9, 14, 26, 0.00) 0%, $neutral-10 50%);
|
||||
}
|
||||
}
|
||||
|
||||
@include media-breakpoint-up(lg) {
|
||||
&__title {
|
||||
font-size: $h3-font-size;
|
||||
line-height: pxToRem(52);
|
||||
}
|
||||
|
||||
&__marquee {
|
||||
display: none;
|
||||
}
|
||||
|
||||
&__list {
|
||||
display: flex;
|
||||
}
|
||||
}
|
||||
}
|
||||
+9
-1
@@ -56,6 +56,7 @@
|
||||
margin-bottom: $spacer * 2;
|
||||
font-size: $h6-font-size;
|
||||
line-height: $spacer * 2;
|
||||
font-weight: $font-weight-light;
|
||||
color: $neutral-20;
|
||||
}
|
||||
|
||||
@@ -134,6 +135,7 @@
|
||||
margin-bottom: 0;
|
||||
font-size: $font-size-l;
|
||||
line-height: $line-height-lg;
|
||||
font-weight: $font-weight-light;
|
||||
color: $neutral-50;
|
||||
}
|
||||
}
|
||||
@@ -157,7 +159,7 @@
|
||||
&__card {
|
||||
height: pxToRem(300);
|
||||
flex-direction: row;
|
||||
padding: $spacer * 3.5;
|
||||
padding: $spacer * 2;
|
||||
|
||||
&-review {
|
||||
justify-content: space-between;
|
||||
@@ -195,4 +197,10 @@
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@include media-breakpoint-up(xl) {
|
||||
&__card {
|
||||
padding: $spacer * 3.5;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,37 +0,0 @@
|
||||
{{ define "main" }}
|
||||
{{ partial "site-header" . }}
|
||||
|
||||
{{ with (.Site.GetPage "e-commerce/hero") }}
|
||||
{{ partial "legal-tech-hero" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/e-commerce/challengers") }}
|
||||
{{ partial "legal-tech-challengers" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/e-commerce/logo-cards") }}
|
||||
{{ .Scratch.Set "additionalLogosClasses" "pt-7 pb-7" }}
|
||||
{{ partial "logo-cards" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/e-commerce/help") }}
|
||||
{{ partial "legal-tech-help" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/e-commerce/get-contacted") }}
|
||||
{{ partial "get-contacted" (dict "context" . "class" "hospitality-and-travel__get-contacted") }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/e-commerce/features") }}
|
||||
{{ partial "legal-tech-features" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/e-commerce/why-qdrant") }}
|
||||
{{ partial "legal-tech-why-qdrant" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/e-commerce/get-started") }}
|
||||
{{ partial "get-started-small" (dict "context" . "class" "get-started-small-rocket") }}
|
||||
{{ end }}
|
||||
|
||||
{{ end }}
|
||||
@@ -1,36 +0,0 @@
|
||||
{{ define "main" }}
|
||||
{{ partial "site-header" . }}
|
||||
|
||||
{{ with (.Site.GetPage "hospitality-and-travel/hero") }}
|
||||
{{ partial "legal-tech-hero" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/hospitality-and-travel/challengers") }}
|
||||
{{ partial "legal-tech-challengers" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/hospitality-and-travel/testimonial-light") }}
|
||||
{{ partial "testimonial-light" (dict "context" . "class" "customers-testimonial pb-5 pb-lg-7") }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/hospitality-and-travel/help") }}
|
||||
{{ partial "legal-tech-help" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/hospitality-and-travel/get-contacted") }}
|
||||
{{ partial "get-contacted" (dict "context" . "class" "hospitality-and-travel__get-contacted") }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/hospitality-and-travel/features") }}
|
||||
{{ partial "legal-tech-features" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/hospitality-and-travel/why-qdrant") }}
|
||||
{{ partial "legal-tech-why-qdrant" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/hospitality-and-travel/get-started") }}
|
||||
{{ partial "get-started-small" (dict "context" . "class" "get-started-small-rocket") }}
|
||||
{{ end }}
|
||||
|
||||
{{ end }}
|
||||
@@ -1 +0,0 @@
|
||||
{{ define "main" }}{{ end }}
|
||||
@@ -1 +0,0 @@
|
||||
{{ define "main" }}{{ end }}
|
||||
@@ -1,36 +0,0 @@
|
||||
{{ define "main" }}
|
||||
{{ partial "site-header" . }}
|
||||
|
||||
{{ with (.Site.GetPage "legal-tech/legal-tech-hero") }}
|
||||
{{ partial "legal-tech-hero" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/legal-tech/legal-tech-challengers") }}
|
||||
{{ partial "legal-tech-challengers" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/legal-tech/legal-tech-testimonials") }}
|
||||
{{ partial "testimonial-cards" (dict "context" .) }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "legal-tech/legal-tech-help") }}
|
||||
{{ partial "legal-tech-help" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/legal-tech/legal-tech-get-contacted") }}
|
||||
{{ partial "get-contacted" (dict "context" . "class" "legal-tech-get-contacted") }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/legal-tech/legal-tech-features") }}
|
||||
{{ partial "legal-tech-features" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "legal-tech/legal-tech-why-qdrant") }}
|
||||
{{ partial "legal-tech-why-qdrant" . }}
|
||||
{{ end }}
|
||||
|
||||
{{ with (.Site.GetPage "/legal-tech/legal-tech-get-started") }}
|
||||
{{ partial "get-started-small" (dict "context" . "class" "get-started-small-rocket") }}
|
||||
{{ end }}
|
||||
|
||||
{{ end }}
|
||||
@@ -1 +0,0 @@
|
||||
{{ define "main" }}{{ end }}
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
<section class="industries-architecture">
|
||||
<div class="container">
|
||||
<h3 class="industries-architecture__title">{{ .Params.title }}</h3>
|
||||
<p class="industries-architecture__description">{{ .Params.description }}</p>
|
||||
<p class="industries-architecture__description">{{ .Params.description | safeHTML }}</p>
|
||||
|
||||
<div class="industries-architecture__sections">
|
||||
{{ range .Params.sections }}
|
||||
|
||||
+2
-2
@@ -4,7 +4,7 @@
|
||||
{{ if .Params.subtitle }}
|
||||
<p class="industries-bento-cards__subtitle">{{ .Params.subtitle }}</p>
|
||||
{{ end }}
|
||||
<h3 class="industries-bento-cards__title">{{ .Params.title }}</h3>
|
||||
<h3 class="industries-bento-cards__title">{{ .Params.title | safeHTML }}</h3>
|
||||
{{ if .Params.description }}
|
||||
<p class="industries-bento-cards__description">{{ .Params.description }}</p>
|
||||
{{ end }}
|
||||
@@ -19,7 +19,7 @@
|
||||
>
|
||||
<img class="industries-bento-cards__card-icon" src="{{ .icon.src }}" alt="{{ .icon.alt }}" />
|
||||
<h6 class="industries-bento-cards__card-title">{{ .title }}</h6>
|
||||
<p class="industries-bento-cards__card-description">{{ .description }}</p>
|
||||
<p class="industries-bento-cards__card-description">{{ .description | safeHTML }}</p>
|
||||
{{ if .chips }}
|
||||
<div class="industries-bento-cards__card-chips">
|
||||
{{ range .chips }}
|
||||
|
||||
+1
-1
@@ -24,7 +24,7 @@
|
||||
<div class="industries-case-studies__slide">
|
||||
<div class="card industries-case-studies__card">
|
||||
<h4 class="industries-case-studies__card-title">{{ .title }}</h4>
|
||||
<p class="industries-case-studies__card-description">{{ .description }}</p>
|
||||
<p class="industries-case-studies__card-description">{{ .description | safeHTML }}</p>
|
||||
</div>
|
||||
</div>
|
||||
{{ end }}
|
||||
|
||||
@@ -6,8 +6,8 @@
|
||||
<img src="{{ .Params.badge.icon.src }}" alt="{{ .Params.badge.icon.alt }}">
|
||||
<span>{{ .Params.badge.title }}</span>
|
||||
</div>
|
||||
<h1 class="industries-hero__title">{{ .Params.title }}</h1>
|
||||
<p class="industries-hero__description">{{ .Params.description }}</p>
|
||||
<h1 class="industries-hero__title">{{ .Params.title | safeHTML }}</h1>
|
||||
<p class="industries-hero__description">{{ .Params.description | safeHTML }}</p>
|
||||
<div class="industries-hero__buttons">
|
||||
<a href="{{ .Params.containedButton.url }}" class="button button_contained button_lg">
|
||||
{{ .Params.containedButton.text }}
|
||||
|
||||
@@ -0,0 +1,38 @@
|
||||
<section class="industries-logos">
|
||||
<div class="container">
|
||||
<div class="row">
|
||||
<div class="col-12">
|
||||
<h2 class="industries-logos__title">{{ .Params.title }}</h2>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="industries-logos__list">
|
||||
{{ range .Params.logos }}
|
||||
<div class="industries-logos__list-item">
|
||||
<img class="industries-logos__list-item-icon" src="{{ .icon.src }}" alt="{{ .icon.alt }}" />
|
||||
</div>
|
||||
{{ end }}
|
||||
</div>
|
||||
<div class="industries-logos__marquee" style="--marquee-items: {{ len .Params.logos }}">
|
||||
<div class="industries-logos__marquee__container">
|
||||
<div class="industries-logos__marquee__block">
|
||||
<div class="industries-logos__marquee__inner industries-logos__marquee__inner_to-left">
|
||||
<span>
|
||||
{{ range .Params.logos }}
|
||||
<div class="industries-logos__marquee__item">
|
||||
<img src="{{ .icon.src }}" alt="{{ .icon.alt }}" />
|
||||
</div>
|
||||
{{ end }}
|
||||
</span>
|
||||
<span>
|
||||
{{ range .Params.logos }}
|
||||
<div class="industries-logos__marquee__item">
|
||||
<img src="{{ .icon.src }}" alt="{{ .icon.alt }}" />
|
||||
</div>
|
||||
{{ end }}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
@@ -117,7 +117,7 @@
|
||||
<script src="{{ $customersJs.RelPermalink }}"></script>
|
||||
{{ end }}
|
||||
|
||||
{{ if eq .Section "hr-tech" }}
|
||||
{{ if eq .Section "industries" }}
|
||||
{{ $customersJs := resources.Get "js/industries.js" | js.Build | minify | resources.Fingerprint "sha512" }}
|
||||
<script src="{{ $customersJs.RelPermalink }}"></script>
|
||||
{{ end }}
|
||||
|
||||
+3
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<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M16.0003 21.3333V28M21.3337 18.6667V28M26.667 13.3333V28M29.3337 4L17.8057 15.528C17.7437 15.5901 17.6702 15.6393 17.5892 15.6729C17.5082 15.7066 17.4213 15.7239 17.3337 15.7239C17.246 15.7239 17.1591 15.7066 17.0781 15.6729C16.9972 15.6393 16.9236 15.5901 16.8617 15.528L12.4723 11.1387C12.3473 11.0137 12.1778 10.9435 12.001 10.9435C11.8242 10.9435 11.6547 11.0137 11.5297 11.1387L2.66699 20M5.33366 24V28M10.667 18.6667V28" stroke="#038585" stroke-width="2.8" stroke-linecap="round" stroke-linejoin="round"/>
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</svg>
|
||||
|
After Width: | Height: | Size: 624 B |
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<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M11.6671 12.6666H6.00043C5.13501 12.6664 4.28669 12.4255 3.55028 11.9709C2.81387 11.5163 2.21838 10.8659 1.83035 10.0923C1.44231 9.31877 1.27703 8.45255 1.35296 7.59047C1.42889 6.72838 1.74305 5.90439 2.26033 5.21057C2.77761 4.51676 3.47763 3.98045 4.28217 3.66158C5.0867 3.34271 5.96406 3.25384 6.8162 3.4049C7.66834 3.55596 8.46168 3.941 9.10759 4.51699C9.75349 5.09298 10.2265 5.83724 10.4738 6.66659H11.6671C12.4627 6.66659 13.2258 6.98266 13.7884 7.54527C14.351 8.10788 14.6671 8.87094 14.6671 9.66659C14.6671 10.4622 14.351 11.2253 13.7884 11.7879C13.2258 12.3505 12.4627 12.6666 11.6671 12.6666Z" stroke="#2F6FF0" stroke-width="1.6" stroke-linecap="round" stroke-linejoin="round"/>
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</svg>
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|
After Width: | Height: | Size: 801 B |
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<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M12 22H18C18.5304 22 19.0391 21.7893 19.4142 21.4142C19.7893 21.0391 20 20.5304 20 20V7L15 2H6C5.46957 2 4.96086 2.21071 4.58579 2.58579C4.21071 2.96086 4 3.46957 4 4V7M14 2V6C14 6.53043 14.2107 7.03914 14.5858 7.41421C14.9609 7.78929 15.4696 8 16 8H20M7 16.5L8 22L5 21L2 22L3 16.5M5 17C5.39397 17 5.78407 16.9224 6.14805 16.7716C6.51203 16.6209 6.84274 16.3999 7.12132 16.1213C7.3999 15.8427 7.62087 15.512 7.77164 15.1481C7.9224 14.7841 8 14.394 8 14C8 13.606 7.9224 13.2159 7.77164 12.8519C7.62087 12.488 7.3999 12.1573 7.12132 11.8787C6.84274 11.6001 6.51203 11.3791 6.14805 11.2284C5.78407 11.0776 5.39397 11 5 11C4.20435 11 3.44129 11.3161 2.87868 11.8787C2.31607 12.4413 2 13.2044 2 14C2 14.7956 2.31607 15.5587 2.87868 16.1213C3.44129 16.6839 4.20435 17 5 17Z" stroke="#2F6FF0" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 964 B |
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<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M14 2V6C14 6.53043 14.2107 7.03914 14.5858 7.41421C14.9609 7.78929 15.4696 8 16 8H20M9 15L11 17L15 13M15 2H6C5.46957 2 4.96086 2.21071 4.58579 2.58579C4.21071 2.96086 4 3.46957 4 4V20C4 20.5304 4.21071 21.0391 4.58579 21.4142C4.96086 21.7893 5.46957 22 6 22H18C18.5304 22 19.0391 21.7893 19.4142 21.4142C19.7893 21.0391 20 20.5304 20 20V7L15 2Z" stroke="#E0700D" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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</svg>
|
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|
After Width: | Height: | Size: 541 B |
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<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M14 2V6C14 6.53043 14.2107 7.03914 14.5858 7.41421C14.9609 7.78929 15.4696 8 16 8H20M10 9H8M16 13H8M16 17H8M15 2H6C5.46957 2 4.96086 2.21071 4.58579 2.58579C4.21071 2.96086 4 3.46957 4 4V20C4 20.5304 4.21071 21.0391 4.58579 21.4142C4.96086 21.7893 5.46957 22 6 22H18C18.5304 22 19.0391 21.7893 19.4142 21.4142C19.7893 21.0391 20 20.5304 20 20V7L15 2Z" stroke="#2F6FF0" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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</svg>
|
||||
|
After Width: | Height: | Size: 547 B |
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<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M22 3H2L10 12.46V19L14 21V12.46L22 3Z" stroke="#DC244C" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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</svg>
|
||||
|
After Width: | Height: | Size: 234 B |
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<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M12 14L16 9.99995M3.34 19C2.46222 17.4798 2.00007 15.7553 2 13.9999C1.99993 12.2445 2.46195 10.52 3.33962 8.99973C4.21729 7.47948 5.47967 6.21705 6.99989 5.33932C8.52011 4.4616 10.2446 3.99951 12 3.99951C13.7554 3.99951 15.4799 4.4616 17.0001 5.33932C18.5203 6.21705 19.7827 7.47948 20.6604 8.99973C21.538 10.52 22.0001 12.2445 22 13.9999C21.9999 15.7553 21.5378 17.4798 20.66 19" stroke="#E0700D" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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</svg>
|
||||
|
After Width: | Height: | Size: 576 B |
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|
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<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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||||
<path d="M12 14L16 9.99995M3.34 19C2.46222 17.4798 2.00007 15.7553 2 13.9999C1.99993 12.2445 2.46195 10.52 3.33962 8.99973C4.21729 7.47948 5.47967 6.21705 6.99989 5.33932C8.52011 4.4616 10.2446 3.99951 12 3.99951C13.7554 3.99951 15.4799 4.4616 17.0001 5.33932C18.5203 6.21705 19.7827 7.47948 20.6604 8.99973C21.538 10.52 22.0001 12.2445 22 13.9999C21.9999 15.7553 21.5378 17.4798 20.66 19" stroke="#DC244C" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 576 B |
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<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
|
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<path d="M14 10L11.9427 7.94267C11.6926 7.69271 11.3536 7.55229 11 7.55229C10.6464 7.55229 10.3074 7.69271 10.0573 7.94267L4 14M3.33333 2H12.6667C13.403 2 14 2.59695 14 3.33333V12.6667C14 13.403 13.403 14 12.6667 14H3.33333C2.59695 14 2 13.403 2 12.6667V3.33333C2 2.59695 2.59695 2 3.33333 2ZM7.33333 6C7.33333 6.73638 6.73638 7.33333 6 7.33333C5.26362 7.33333 4.66667 6.73638 4.66667 6C4.66667 5.26362 5.26362 4.66667 6 4.66667C6.73638 4.66667 7.33333 5.26362 7.33333 6Z" stroke="#2F6FF0" stroke-width="1.6" stroke-linecap="round" stroke-linejoin="round"/>
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||||
</svg>
|
||||
|
After Width: | Height: | Size: 661 B |
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<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
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<g clip-path="url(#clip0_17400_5899)">
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<path d="M1.33301 7.99992H3.33301M3.33301 7.99992C3.33301 10.5772 5.42235 12.6666 7.99967 12.6666M3.33301 7.99992C3.33301 5.42259 5.42235 3.33325 7.99967 3.33325M12.6663 7.99992H14.6663M12.6663 7.99992C12.6663 10.5772 10.577 12.6666 7.99967 12.6666M12.6663 7.99992C12.6663 5.42259 10.577 3.33325 7.99967 3.33325M7.99967 1.33325V3.33325M7.99967 12.6666V14.6666M9.99967 7.99992C9.99967 9.10449 9.10424 9.99992 7.99967 9.99992C6.8951 9.99992 5.99967 9.10449 5.99967 7.99992C5.99967 6.89535 6.8951 5.99992 7.99967 5.99992C9.10424 5.99992 9.99967 6.89535 9.99967 7.99992Z" stroke="#2F6FF0" stroke-width="1.6" stroke-linecap="round" stroke-linejoin="round"/>
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</g>
|
||||
<defs>
|
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<clipPath id="clip0_17400_5899">
|
||||
<rect width="16" height="16" fill="white"/>
|
||||
</clipPath>
|
||||
</defs>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 904 B |
@@ -0,0 +1,3 @@
|
||||
<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M8.00033 10.0001L6.00033 8.00008M8.00033 10.0001C8.93156 9.64591 9.82492 9.19923 10.667 8.66675M8.00033 10.0001V13.3334C8.00033 13.3334 10.0203 12.9667 10.667 12.0001C11.387 10.9201 10.667 8.66675 10.667 8.66675M6.00033 8.00008C6.35509 7.0797 6.80179 6.19746 7.33366 5.36675C8.11045 4.12474 9.19207 3.10212 10.4757 2.39614C11.7592 1.69017 13.2021 1.32433 14.667 1.33342C14.667 3.14675 14.147 6.33342 10.667 8.66675M6.00033 8.00008H2.66699C2.66699 8.00008 3.03366 5.98008 4.00033 5.33342C5.08033 4.61342 7.33366 5.33342 7.33366 5.33342M3.00033 11.0001C2.00033 11.8401 1.66699 14.3334 1.66699 14.3334C1.66699 14.3334 4.16033 14.0001 5.00033 13.0001C5.47366 12.4401 5.46699 11.5801 4.94033 11.0601C4.6812 10.8128 4.33985 10.6698 3.98181 10.6588C3.62376 10.6477 3.27424 10.7692 3.00033 11.0001Z" stroke="#038585" stroke-width="1.6" stroke-linecap="round" stroke-linejoin="round"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 989 B |
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|
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<svg width="20" height="20" viewBox="0 0 20 20" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
<path d="M5.83366 17.5H14.167M10.0003 2.5V17.5M2.50033 5.83333H4.16699C5.83366 5.83333 8.33366 5 10.0003 4.16667C11.667 5 14.167 5.83333 15.8337 5.83333H17.5003M13.3337 13.3333L15.8337 6.66667L18.3337 13.3333C17.6087 13.875 16.7337 14.1667 15.8337 14.1667C14.9337 14.1667 14.0587 13.875 13.3337 13.3333ZM1.66699 13.3333L4.16699 6.66667L6.66699 13.3333C5.94199 13.875 5.06699 14.1667 4.16699 14.1667C3.26699 14.1667 2.39199 13.875 1.66699 13.3333Z" stroke="#2F6FF0" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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||||
</svg>
|
||||
|
After Width: | Height: | Size: 634 B |
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<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M8 11L10 13L14 9M21 21L16.7 16.7M19 11C19 15.4183 15.4183 19 11 19C6.58172 19 3 15.4183 3 11C3 6.58172 6.58172 3 11 3C15.4183 3 19 6.58172 19 11Z" stroke="#2F6FF0" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
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</svg>
|
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|
After Width: | Height: | Size: 342 B |
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<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
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</svg>
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||||
|
After Width: | Height: | Size: 796 B |
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<svg width="20" height="20" viewBox="0 0 20 20" fill="none" xmlns="http://www.w3.org/2000/svg">
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</svg>
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|
After Width: | Height: | Size: 870 B |
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<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
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|
||||
<path d="M7.99967 11.9999C10.2088 11.9999 11.9997 10.2091 11.9997 7.99992C11.9997 5.79078 10.2088 3.99992 7.99967 3.99992C5.79054 3.99992 3.99967 5.79078 3.99967 7.99992C3.99967 10.2091 5.79054 11.9999 7.99967 11.9999Z" stroke="#2F6FF0" stroke-width="1.6" stroke-linecap="round" stroke-linejoin="round"/>
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||||
<path d="M7.99967 9.33325C8.73605 9.33325 9.33301 8.7363 9.33301 7.99992C9.33301 7.26354 8.73605 6.66658 7.99967 6.66658C7.26329 6.66658 6.66634 7.26354 6.66634 7.99992C6.66634 8.7363 7.26329 9.33325 7.99967 9.33325Z" stroke="#2F6FF0" stroke-width="1.6" stroke-linecap="round" stroke-linejoin="round"/>
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|
After Width: | Height: | Size: 1016 B |
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<svg width="112" height="40" viewBox="0 0 112 40" fill="none" xmlns="http://www.w3.org/2000/svg">
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Reference in New Issue
Block a user