diff --git a/qdrant-landing/content/course/_index.md b/qdrant-landing/content/course/_index.md
index 4072196c1..d3ed30a61 100644
--- a/qdrant-landing/content/course/_index.md
+++ b/qdrant-landing/content/course/_index.md
@@ -1,5 +1,5 @@
---
-title: "Welcome to Qdrant Academy"
+title: "Qdrant Academy"
description: Master vector search and AI-powered applications with Qdrant Academy. Free, self-paced courses guide you from beginner to expert with hands-on projects, code notebooks, and certification.
weight: 50
---
@@ -12,13 +12,11 @@ Qdrant Academy is your step-by-step learning hub for mastering vector search, hy
Whether you’re new to Qdrant or building production-grade systems, our guided courses help you go from beginner to expert, one module at a time.
-Qdrant Academy has just launched in Fall of 2025 and currently offers one comprehensive course, but more are on the way! Register your interest in each upcoming below.
-
## Available Now
-{{< course-card
+{{< course-card
title="Qdrant Essentials Course"
- image="/icons/outline/rocket-white-light.svg"
+ image="/icons/outline/rocket-white-light.svg"
link="/course/essentials/"
>}}
**What you’ll gain:**
@@ -30,7 +28,24 @@ Qdrant Academy has just launched in Fall of 2025 and currently offers one compre
- Ecosystem Integrations (Bonus)
Time to Complete: 9-12 hours
-Includes: videos, code notebooks, projects, walkthroughs
+Includes: videos, code notebooks, projects, certification
+{{< /course-card >}}
+
+{{< course-card
+ title="Multi-Vector Search Course"
+ image="/icons/outline/similarity-blue.svg"
+ link="/course/multi-vector-search/"
+>}}
+**What you’ll gain:**
+- Late Interaction Models and MaxSim Scoring
+- ColBERT for Text Search
+- ColPali for Visual Document Search
+- Multi-Stage Retrieval Pipelines
+- Quantization and Pooling Techniques
+- MUVERA Indexing for Large-Scale Search
+
+Time to Complete: 4-6 hours
+Includes: videos, code notebooks, projects, certification
{{< /course-card >}}
## Upcoming Courses
diff --git a/qdrant-landing/content/course/multi-vector-search/_index.md b/qdrant-landing/content/course/multi-vector-search/_index.md
index 4231a9590..e7bc54d0d 100644
--- a/qdrant-landing/content/course/multi-vector-search/_index.md
+++ b/qdrant-landing/content/course/multi-vector-search/_index.md
@@ -146,11 +146,11 @@ ML, backend, and search engineers who want to go beyond single-vector embeddings
## Time commitment
-- Duration: 4 modules at 2-3 hours/module
-- Video learning: ~4 hours
-- Hands-on notebooks: ~4 hours
-- Final project: 2-4 hours
-- Total: 8-12 hours
+- Duration: 3 modules at 1 hour/module
+- Video learning: 1.5 hours
+- Hands-on notebooks: 1.5 hours
+- Final project: 1-3 hours
+- Total: 4-6 hours
{{< course-card
diff --git a/qdrant-landing/content/course/multi-vector-search/certification.md b/qdrant-landing/content/course/multi-vector-search/certification.md
new file mode 100644
index 000000000..48f80afe1
--- /dev/null
+++ b/qdrant-landing/content/course/multi-vector-search/certification.md
@@ -0,0 +1,25 @@
+---
+title: "Qdrant Multi-Vector Certification"
+description: "Get officially certified in multi-vector search by Qdrant."
+url: /course/multi-vector-search/certification/
+weight: 50
+---
+
+# Qdrant Multi-Vector Search Certification
+
+Congratulations! You’ve completed the **Multi-Vector Search course**. You didn’t just learn how to store vectors; you learned how to build high-performance retrieval systems using late interaction models and multi-vector representations.
+
+You’ve moved past single-vector embeddings and dove deep into ColBERT, ColPali, MaxSim scoring, MUVERA, and production-grade multi-vector pipelines. That effort deserves more than just a “finished” status. It deserves professional recognition!
+
+## Get #QdrantCertified
+
+Your expertise is now production-ready. It’s time to validate those skills with our official certification.
+
+Head over to [train.qdrant.dev](https://train.qdrant.dev) to take the exam.
+
+Passing this exam proves you aren’t just a user; you are a Search Engineer capable of:
+
+- Designing multi-vector retrieval pipelines with late interaction models.
+- Applying ColPali and its variants for visual document search.
+- Optimizing multi-vector search for both memory and latency.
+- Mastering MaxSim scoring and the nuances of multi-vector architectures in Qdrant.
\ No newline at end of file
diff --git a/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md b/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md
index ce78041d7..46ad20f8d 100644
--- a/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md
+++ b/qdrant-landing/content/course/multi-vector-search/module-1/late-interaction-basics.md
@@ -16,7 +16,7 @@ This lesson introduces the late interaction paradigm - the foundation of multi-v