Vector Index Overview
Semantic and similarity search.
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CASE 36
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Vector Database Infrastructure for Semantic Search
Project overview
From operating need to a usable digital system.
A vector storage layer for embeddings, meaning-based retrieval, similarity search, and RAG knowledge access.
Business challenge
Operations relied on manual steps and data scattered across tools. The solution focused on integration, observability, and control over data and model execution. Semantic and similarity search.
Critical flowInformation retrieval for AI assistants.
Solution approach
What we designed and delivered
Product structure, workflows and operational controls translated into an executable experience.
- Semantic and similarity search.
- Information retrieval for AI assistants.
- RAG, recommendations, and similarity-based classification.
- Shared use across applications while retaining data on the server.
- Separation of application logic, models, vector storage, and traditional databases.
Technology & capability
QdrantEmbeddingsSemantic SearchRAGAI Retrieval
A usable, scalable operating foundation was delivered, reducing manual steps and improving observability.
UI / UX journey
Interfaces built around real roles and tasks
Concept UI/UX visualizations based on the documented project scope.
Data Sources
Information retrieval for AI assistants.
Search Playground
RAG, recommendations, and similarity-based classification.
Relevance Analytics
Shared use across applications while retaining data on the server.
Design gallery
Scroll Vector Search UI/UX
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