Technical Deep Dive
Verified specifications, architectural trade-offs, and measurable outcomes.
Cognitive Document Search Engine
The Challenge
Internal knowledge teams spent hours searching across hundreds of thousands of unstructured policy documents, contracts, and audit reports.
The Technical Solution
Developed an automated ingestion pipeline extracting document semantics, chunking with overlap, creating embeddings, and indexing in a low-latency vector database.
Architecture & Stack Specifications
Python 3.11, FastAPI, Qdrant / pgvector, LangChain, OpenAI / Claude API Gateways, Docker
Measured Results & Impact
98% relevance match score; 1.2s average semantic query latency; 75% reduction in internal research time.
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