Enterprise retrieval-augmented generation platform
An experimental internal platform that grounds language-model answers in trusted company information through semantic retrieval and controlled context.
The problem
General-purpose language models cannot be trusted to know private, current enterprise information. The platform needed to retrieve the right internal context, limit hallucinations, and remain compatible with secure hosting constraints.
Approach
Kamal implemented the Next.js frontend and Python APIs, then designed a retrieval pipeline using vector embeddings and semantic search. The answer-generation step receives only the context selected by that pipeline.
- Applied controlled chunking with overlap to preserve useful context.
- Used cross-encoder reranking to prioritize the most relevant passages.
- Required references to source lines so answers could be checked.
- Investigated secure hosting and deployment options for enterprise AI workloads.
Outcome
The prototype established a practical foundation for answers that are more relevant, explainable, and grounded than an unassisted model response. Confidential company data and internal performance measurements are intentionally not disclosed.