The aim was one answer a team could trust and check, not another list of results. Here is how DocuMind differs from the search it replaces.


Mapped the failure modes of generic chatbot search, hallucinated answers with no traceability, onto an ingestion, retrieval, rerank and generation pipeline.
Built the background chunking and embedding pipeline so large PDF, policy and wiki collections index without blocking active use.
Implemented combined keyword and dense vector search over the embedded document store to catch exact-term and meaning-based matches.
Added reranking ahead of generation, then constrained the model to answer only from reranked passages with an inline citation.
Built the conversational front end and the dashboard tracking document volume, query trends and citation activity.
Validated retrieval accuracy and ingestion throughput against large, growing document sets, well beyond a small demo collection.
