AI case study — Knowledge management

A retrieval-augmented system that makes 2,000+ PDF documents of SOPs and business standards genuinely searchable — context-aware answers with sources, not keyword roulette.

RAGVector searchLLM
2,000+PDFs turned into a queryable knowledge base
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The challenge

What we walked into.

The client needed a smarter way to search more than 2,000 PDFs of Standard Operating Procedures and business standards. Traditional keyword search kept missing context — finding the page is not the same as finding the answer.

What we built

The solution.

01

A Retrieval-Augmented Generation system over the full document corpus.

02

Document retrieval finds the most relevant sections from the PDFs; a generative model reads them and produces clear, context-aware answers.

03

Answers are grounded in the retrieved text rather than the model's memory — precise and reliable.

The results

What changed.

01

Teams query thousands of pages of SOPs in plain language and get sourced answers in seconds.

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