A representative engagement showing how we deliver enterprise AI responsibly: a semantic knowledge engine that lets teams find grounded answers across large document sets.
Grounded
Cited answers
Semantic
Hybrid retrieval
Evaluated
Measured quality
The Challenge
Knowledge was scattered across thousands of documents, and keyword search returned noise. Teams wasted time hunting for answers, and leadership wanted AI that was accurate, grounded, and safe.
Our Approach
We built a retrieval-augmented pipeline: documents chunked and embedded into a vector store, hybrid semantic plus keyword retrieval, and an LLM answering with citations. We added an evaluation harness and guardrails for safety and PII.
Outcomes
- Grounded answers with source citations
- Dramatically faster knowledge retrieval
- Evaluation harness keeping quality measurable
- Guardrails for safety and data protection
Technology Used
PythonLangChainpgvectorOpenAI / AnthropicFastAPI