RAG Consulting & Cited Document Assistants
A general chatbot will confidently invent an answer. For documents you can't afford to get wrong — policies, regulations, manuals, contracts — you need retrieval-augmented generation with a citation behind every claim. We build those systems, prove they're accurate, and run them for you.
The problem with answers you can't check
Large language models are fluent, which is exactly why an unsourced answer is dangerous: a wrong reply reads as convincingly as a right one. When the question is “what does our policy say” or “is this allowed under the regulation,” fluency isn't enough — someone has to be able to verify it.
Retrieval-augmented generation (RAG) fixes this by grounding the model in your actual documents and forcing every answer to point at the passage it came from. Done well, the user gets a fast answer and the receipt to back it up.
What a cited document assistant includes
Citation enforcement
Every answer links to the specific document, section, and passage it came from. The requirement is built into the architecture, so an answer without a source doesn't ship.
Retrieval tuned to your corpus
We tune how documents are chunked, indexed, and retrieved for your material — a contract set behaves differently from a maintenance manual.
Confidence and uncertainty handling
When the documents don't contain the answer, the system says so instead of guessing. Knowing what it doesn't know is part of being trustworthy.
Evaluation suite
Automated tests against a known-good question set catch quality regressions before users do, and give you a number to track over time.
Three cited assistants you can try
Same architecture, three different corpora — from a single rulebook to a regulated-document archive.
USGA Rules Expert
Golf-rules Q&A grounded in the Rules of Golf, with the exact rule cited on every answer.
Compliance Copilot
Policy and procedure assistant that triages questions and answers with sources across many documents.
Pharma Intelligence
Retrieval over FDA warning letters and regulated documents, with citations and trend analysis.
This is one of two lanes we work in. See the bigger picture in AI implementation consulting.
Frequently asked questions
What is retrieval-augmented generation (RAG)?+
Retrieval-augmented generation is an approach where the AI first retrieves the most relevant passages from your own documents, then writes an answer grounded in that retrieved text. Instead of relying on what a model memorized during training, it answers from your current, authoritative material — and can point to exactly which passage it used.
How do you stop the AI from hallucinating or making things up?+
Two things. First, the model is constrained to answer from retrieved source passages rather than from open-ended generation. Second, citation is enforced at the architecture level, so an answer is tied to a source or it isn't returned. We also run an evaluation suite that measures how often answers are grounded and correct.
Can the assistant cite its sources?+
Yes — that's the point. Every answer traces back to a specific document, section, and passage, and the user can open the source to verify it. For regulated or high-stakes work, that verifiable trail is usually the difference between a tool people trust and one they don't.
How is this different from ChatGPT or a generic chatbot?+
A generic chatbot answers from its training data and has no reliable way to show where an answer came from. A cited document assistant answers from your specific corpus and shows its evidence. For internal knowledge that changes over time and carries real consequences, that grounding and traceability matter more than raw fluency.
What kinds of documents can it work with?+
Policies, procedures, regulations, contracts, technical manuals, research archives — any text-heavy corpus where finding the right passage is slow and getting it wrong is costly. We handle ingestion, parsing, and chunking as part of the build.
How long does it take to build a RAG system?+
After a 2–3 week discovery, a focused first build for one corpus and use case typically takes 4–8 weeks, ending with a production system, citations, and an evaluation suite. We scope to a single corpus first so you get something real quickly rather than waiting on a sprawling rollout.
Have a document corpus worth searching?
Tell us what your team keeps looking up and getting wrong. We'll tell you whether a cited assistant is the right fit.
Describe your challenge