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RAG Development

Make AI answer from your knowledge, not the internet’s. We build retrieval-augmented generation systems that find the right information, respect access rights, and show where every answer came from.

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Most knowledge assistants fail at retrieval, not generation.

If the system finds the wrong document, even the best model gives the wrong answer.

  • Scattered knowledge

    Information lives across drives, wikis, tickets, PDFs, and databases.

  • Messy documents

    Tables, scans, and long policies are hard to split and search accurately.

  • Stale or conflicting content

    Old versions surface alongside current ones, and users can’t tell which to trust.

  • Permission leaks

    A shared index can expose information to people who shouldn’t see it.

Every layer of a dependable RAG system.

  • Source connectors & ingestion

    Bring in content from document stores, knowledge bases, ticketing tools, and databases, and keep it in sync.

  • Document parsing & chunking

    Handle PDFs, tables, and long documents so the meaning survives indexing.

  • Hybrid search & reranking

    Combine semantic and keyword search, then rerank results for relevance.

  • Access-aware retrieval

    Apply each user’s permissions at query time so answers only use what they can see.

  • Cited answer generation

    Generate responses with references back to the exact sources used.

  • Retrieval evaluation

    Measure whether the right sources are found and answers stay faithful to them.

How we build RAG systems.

  1. Audit

    Review your sources, formats, permissions, and the questions users need answered.

    Learn more about Audit

  2. Prototype

    Index a representative set of content and test answers against real questions.

    Learn more about Prototype

  3. Scale

    Connect all sources, add permissions and monitoring, and integrate with your tools.

    Learn more about Scale

  4. Maintain

    Keep content fresh, track quality, and improve retrieval over time.

    Learn more about Maintain

Where RAG makes the difference.

Questions about RAG?

What is retrieval-augmented generation?

RAG finds relevant passages from your own content and gives them to a language model, which writes an answer based on those sources and can cite them.

Why not just fine-tune a model on our documents?

Retrieval keeps answers current as content changes, supports citations, and can respect user permissions. Fine-tuning is better suited to style or specialized behavior.

Can it handle scanned documents and tables?

Yes, with the right parsing. We test your actual document types during the prototype.

How do you know the answers are accurate?

We build evaluation sets from real questions and measure both whether the right sources are retrieved and whether answers stay faithful to them.

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