Vector database for AI document search
Edifycode designed a semantic search stack that helped teams find internal information faster and laid the foundation for retrieval-backed assistants and analytics workflows.
Need
The client had growing document volume and needed a better retrieval layer than keyword search alone.
Build
Edifycode implemented semantic indexing, retrieval logic, and application flows for knowledge access.
Value
Search quality improved, retrieval became more usable, and later AI assistant work had a stronger foundation.
How this connected to the wider stack
The project was not only about storing embeddings. It included content structure, document ingestion, ranking strategy, and the interfaces that made search practical for real teams. That is why the work fits both search infrastructure and applied AI delivery.
Related pages: AI chatbot with RAG integration, AI software development, and all project case studies.
Related links
AI chatbot with RAG integration
A downstream application of retrieval-backed infrastructure.
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