DOCUMENT Q&A INTELLIGENCE

A retrieval system that reads an entire document library and answers questions in plain language, with every answer traced to its source.

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Case detail
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Project challenges

Answers buried across thousands of pages are slow to find and easy to get wrong. Language models will confidently invent detail whenever retrieval comes up short.

  • Answers buried in thousands of pages.
  • Splitting documents without losing meaning.
  • Stopping the model inventing what it cannot find.
  • Keeping responses fast enough to be usable.
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The solution

We built a system that reads the whole document set, finds the passages that actually answer the question, and writes back an answer. Every response is checked against its sources before itis shown.

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The result

Answers now arrive in seconds, grounded in the source text, with the reasoning open to inspection.

30

%

Reduction in response latency

100

%

Answers traced end to end

3200

Document pages indexed

Project information

Date:
Jun 2026
Client:
Internal Product
Industry:
Engineering & Construction
Services:
ML, NLP, RAG Development, AI Chatbot Development, LLM Evaluation
Technology stack:
Python, LangChain, FAISS, LangSmith, OpenAI, Sentence Transformers, Streamlit

“A confident wrong answer is worse than no answer. We measured faithfulness before speed.”

Shazin Ansari
AI Systems Engineer, Wevier AI
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