Update case-study-my-askai.md

qdrant-landing/content/blog/case-study-my-askai.md

@@ -1,6 +1,6 @@
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-title: "How My AskAI Built Self-Improving Support Agents with Qdrant"
+title: "How My AskAI Built Self-Improving Support Agents"
 short_description: "My AskAI scaled reliable support agents on Qdrant Cloud."
 description: "Discover how My AskAI built a self-improving customer support agent platform with Qdrant Cloud, enabling scalable retrieval, hybrid search iteration, and faster operations."
 preview_image: /blog/case-study-my-askai/social_preview_partnership-my-askai.png
@@ -23,7 +23,7 @@ tags:
 
 [My AskAI](https://myaskai.com) built a managed platform for AI customer support agents that plug directly into existing helpdesk tools like [Intercom](https://myaskai.com/ai-agent-integration/intercom) and [Zendesk](https://myaskai.com/ai-agent-integration/zendesk-tickets). The goal was to make AI behave like a reliable coworker, not a brittle chatbot. In production, My AskAI's agents are designed to resolve a large portion of inbound support requests automatically, then hand over to a human when the agent cannot answer confidently. My AskAI positions this as [deflecting around 75 percent of support requests](https://myaskai.com/blog/my-askai-edel-optics-case-study-2026) and sustaining a resolution rate in the low to mid 70s, depending on the time window and workload mix.
 
-As My AskAI narrowed its focus, the team discovered that customer support was not just a use case, it was the use case. Support data is messy, unstructured, and constantly changing. Success required strong retrieval, predictable latency, and an infrastructure layer that could scale without forcing the team to become full-time database operators. That combination ultimately led My AskAI to standardize on [Qdrant Cloud](http://cloud.qdrant.io) as the vector search backbone of its platform.
+As My AskAI narrowed its focus, the team discovered that customer support was not just a use case; it was **the use case**. Support data is messy, unstructured, and constantly changing. Success required strong retrieval, predictable latency, and an infrastructure layer that could scale without forcing the team to become full-time database operators. That combination ultimately led My AskAI to standardize on [Qdrant Cloud](http://cloud.qdrant.io) as the vector search backbone of its platform.
 
 ## Customer Support Created Unique Retrieval Requirements
 
@@ -31,7 +31,7 @@ My AskAI started as a broader "chat with your data" product. Users could connect
 
 That analysis showed a clear pattern.
 
-"When we double clicked on our users, the stickiest users and the ones generating the most revenue, we saw that pretty much all of them were using it in a customer support use case," explains Alex, co-founder of My AskAI. "So we thought, there's something interesting happening here. Let's focus on this niche."
+"When we double clicked on our users, the stickiest users and the ones generating the most revenue, we saw that pretty much all of them were using it in a customer support use case," explains Alex Rainey, co-founder of My AskAI. "So we thought, there's something interesting happening here. Let's focus on this niche."
 
 These teams cared about accuracy, speed, and guardrails because every answer represented their brand. In practice, that meant My AskAI needed to index and retrieve from large sets of unstructured documents and historical support interactions. Keyword search alone was not enough. Customers often described issues differently than the help article headings, and support tickets were full of partial context and inconsistent phrasing. My AskAI needed semantic retrieval that could match intent, not just exact terms.
 
@@ -39,13 +39,12 @@ These teams cared about accuracy, speed, and guardrails because every answer rep
 
 My AskAI's earliest attempts at working with customer data hit a wall quickly. Before embedding models were widely available, the team tried fine-tuning early language models on customer support tickets to generate answers to new questions.
 
->"We tried to do some fine-tuning, which was obviously the wrong way to approach the problem.Fine-tuning on customer support tickets to answer new questions just didn't make sense. It was very early days."
- Alex Rainey - CTO - My AskAI
+>"We tried to do some fine-tuning, which was obviously the wrong way to approach the problem. Fine-tuning on customer support tickets to answer new questions just didn't make sense. It was very early days."
+
 
 Everything changed when OpenAI released its embedding model. Instead of hoping users would guess the right keyword, My AskAI could retrieve relevant passages by semantic similarity and pass them into an LLM as context. This became the basis of My AskAI's customer support workflow.
 
 >"That was a transformational moment for us. Now we could have hundreds of help articles ingested in the system, and a user can ask a question, and we can answer that really specifically and cheaply and quickly."
-Alex Rainey - CTO - My AskAI
 
 
 Over time, the team also learned that semantic search was strong but not universally sufficient, especially when tickets contained product names, error codes, or specific identifiers that benefit from lexical matching. That realization led My AskAI toward experimentation with [hybrid search](https://qdrant.tech/documentation/concepts/hybrid-queries/) as a way to blend semantic similarity with keyword signals, while keeping the operational footprint small.
@@ -62,7 +61,7 @@ Second, the platform needed to integrate smoothly into an ecosystem of connector
 
 Third, the developer experience had to be straightforward so the team could scale and manage instances without constant engineering effort.
 
-The team also leaned on community signal to validate the decision. "A lot of developers were building in public, speaking about exactly what infra they were using under the hood," Alex says. "I just kept seeing Qdrant coming up. Engineers at our third-party connector tool spoke extremely highly of Qdrant for scalability and latency."
+The team also leaned on community signal to validate the decision. "A lot of developers were building in public, speaking about exactly what infra they were using under the hood. I just kept seeing Qdrant coming up. Engineers at our third-party connector tool spoke extremely highly of Qdrant for scalability and latency."
 
 The migration itself was smooth, in part because My AskAI used the transition to build a V2 of their product focused purely on customer support. That gave them a clean start with Qdrant and a modernized stack, rather than requiring a large-scale data migration.
 
@@ -70,17 +69,17 @@ The migration itself was smooth, in part because My AskAI used the transition to
 
 Once on Qdrant Cloud, My AskAI leaned into a workflow where scaling and day-to-day operations were simple. The team relied on the dashboard for performance visibility and managed scaling with a few UI actions, while still using the API for collection setup and configuration when needed.
 
->"Scaling horizontally or vertically is like two clicks away. It's not really a concern we have. If we notice anything, we get the alert, and we can jump in and scale things up or down as we need to." Alex Rainey - CTO - My AskAI
+>"Scaling horizontally or vertically is like two clicks away. It's not really a concern we have. If we notice anything, we get the alert, and we can jump in and scale things up or down as we need to." 
 
 The ideal infrastructure, as Alex puts it, is the kind you don't have to think about. "I didn't want to have to think about it."
 
 My AskAI also began running customer-specific proofs of concept for [hybrid search](https://qdrant.tech/documentation/concepts/hybrid-queries/), aiming to find the right blend that improved retrieval in the edge cases where semantic-only results were not enough. Before Qdrant, managing hybrid search had required spinning up separate infrastructure on AWS and handling reranking externally. With Qdrant, the team could enable hybrid search per collection and iterate without managing additional systems.
 
->"Just being able to turn on hybrid search is super useful. It removes that headache and pushes management of hybrid search down to the vendor." Alex Rainey - CTO - My AskAI
+>"Just being able to turn on hybrid search is super useful. It removes that headache and pushes management of hybrid search down to the vendor."
 
 Just as importantly, My AskAI highlighted the hands-on, highly technical support experience as part of why the platform felt dependable for production workloads.
 
-"Qdrant support is always phenomenal. Super fast, super technical, very hands on. We've always had great experiences whenever we needed them." Alex, My AskAI
+"Qdrant support is always phenomenal. Super fast, super technical, very hands on. We've always had great experiences whenever we needed them."
 
 ## What's Next: Self-Learning Support Agents Powered by Clustered Knowledge