Knowledge · Retrieval system

Answers grounded in your corpus, with a citation on every claim

We built a retrieval system over a large private corpus that answers questions in plain language and cites the exact passage behind every sentence. When the answer isn't in the corpus, it says so instead of inventing one.

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The challenge

The knowledge existed, scattered across thousands of internal documents, but finding it meant knowing which file to open and who to ask. Off-the-shelf search returned keyword matches, not answers, and the team had watched generic assistants confidently make things up. They needed answers they could trust enough to act on.

What we built
  • An ingestion pipeline that chunks documents along their real structure, preserving headings, tables, and context instead of slicing blindly.
  • Hybrid retrieval that combines semantic and keyword search, then reranks, so the right passage surfaces whether the user knows the exact term or not.
  • Grounded generation that cites the source passage behind each claim and abstains when the corpus doesn't support an answer.
  • An evaluation harness that scores retrieval and answer quality against a labelled set, so changes are measured, not guessed.
The outcome
  • Every answer links to the passage it came from, so users verify instead of trust blindly.
  • The system declines to answer when the corpus is silent, rather than fabricating.
  • People find answers in seconds without knowing which document or person to ask.
FAQ

Common questions

Grounded generation cites the source passage behind each claim and abstains when the corpus doesn't support an answer. The system is built to say it doesn't know rather than invent one, so a confident fabrication, the usual failure of generic assistants, is designed out.

Because users don't always know the exact term. We combine semantic and keyword search, then rerank, so the right passage surfaces whether someone searches by meaning or by a precise phrase. Keyword-only search returns matches; this returns answers with the passage behind them.

We build an evaluation harness that scores retrieval and answer quality against a labelled set. Changes are measured rather than guessed, so when we tune chunking or reranking we can prove the system got better instead of hoping it did.

The ingestion pipeline chunks documents along their real structure, keeping headings, tables, and context intact instead of slicing blindly at a fixed length. Better chunks mean the retrieved passage actually contains the answer, which is what makes the citations trustworthy.

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