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RAG systems that answer from your documents — and cite them

Retrieval-augmented generation (RAG) connects an AI assistant to your actual documentation, so answers come from your approved sources instead of the model’s guesswork. I design, benchmark, and deploy RAG systems for US small and mid-sized businesses — with citations, evaluation, and guardrails built in from the start.

Sound familiar?

Generic AI makes things up

Off-the-shelf chatbots answer confidently from training data, not your policies — and you can’t tell where an answer came from.

Search that doesn’t find

Keyword search across wikis, PDFs, and drives misses answers that are phrased differently than the question.

A demo that stalled

A proof-of-concept worked on ten documents, but accuracy fell apart on the real corpus and nobody knows which knob to turn.

No way to measure quality

Without an evaluation set, every configuration change is a gamble — you can’t tell whether a tweak helped or quietly made answers worse.

How I approach it

1

Ground it in your sources

Your handbooks, SOPs, policies, and knowledge bases are processed into a retrieval index, with access controls reflecting who may see what.

2

Benchmark the configuration, don’t guess it

Chunking, retrieval technique, reranking, and prompt design are tested against an evaluation set built from real questions your team asks. Configuration choices are backed by measurements, not defaults.

3

Make every answer auditable

Answers cite the source passages they came from, and the system says “I don’t know” when the documentation doesn’t support a confident answer.

4

Deploy where your risk profile allows

Open-source models on your infrastructure, a private cloud, or a managed API — the trade-offs are documented and the choice stays yours.

What you get

  • A working RAG assistant integrated with your existing tools (Slack, Teams, help desk, intranet)
  • An evaluation set and measured accuracy baseline you can re-run after any change
  • Citations and activity logging for every answer
  • Implementation notes, handoff documentation, and training for your team

Backed by published testing

Reclaiming AI Document Search Quality Through Configuration Testing And Parameter Sweeps

Four rounds of systematic testing — 16,143 evaluations across 40+ configurations — produced a single evidence-based RAG setup, overturning several "best practice" assumptions along the way.

Read the full study

Fit check: RAG works best on text-based documentation (handbooks, SOPs, manuals, wikis). If your knowledge lives mostly in images, video, or tribal memory, I’ll tell you during the free discovery — before you spend anything.

Find out what this would look like for your team

The consultation and initial discovery are free — you get a preliminary recommendation whether or not we work together.

Book a Free Consultation