AI·RAG · Vector Search

Enterprise RAG Assistant

CLIENT  Vantage Labs/YEAR  2024/TIMELINE  8 weeks

Knowledge management system that indexes 40,000+ internal documents and delivers accurate, source-cited answers to employee queries in real time.

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AI · CASE STUDYVantage Labs
40k+
Documents indexed
8 wks
Data to live
94%
Answer accuracy
70%
Tickets reduced

The Challenge

Vantage Labs had a decade of institutional knowledge trapped in PDFs, emails, and Confluence pages. New hires took months to get up to speed. Experts spent hours answering repetitive questions.

Our Solution

Built a hybrid retrieval pipeline (dense + sparse search), custom chunking strategy for technical documents, and a citation system that makes every answer verifiable.

THE OUTCOME

What we shipped.

Support ticket volume dropped 70% in the first month. Answer accuracy benchmarked at 94% against a manually curated test set. New hire ramp time cut from 3 months to 3 weeks.

Technology Stack

OpenAIPineconeLangChainPythonFastAPIReact

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FAQ

About this engagement.

Vantage Labs benchmarked at 94% answer accuracy against a curated test set, with every answer carrying a source citation so users can verify it.
Hybrid retrieval (dense + sparse) grounds every answer in your own documents rather than the model's training data, and the citation system makes each response traceable to its source.
Vantage Labs went from raw data to a live system in 8 weeks. Timelines scale with data volume, document complexity, and the number of integrations.
Yes — PDFs, wikis, emails, and more. We build a custom chunking and indexing pipeline tuned to your content so retrieval stays accurate on technical material.

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