RAG that quotes your data, not the internet.

Most RAG demos break the day real users arrive with messy questions. We tune retrieval against your actual queries, ship with eval harnesses, and monitor accuracy long after the launch slide deck is forgotten.

RAG system grounding an AI answer in cited source documents

Why our RAG ships in production.

98%
Accuracy on real queries
<300ms
Median Response Time
100%
Cited answers
24/7
Continuous evals
What RAG actually is

RAG is not a model. It's a pipeline.

Most people picture RAG as ChatGPT with your docs attached. It isn't. A RAG system has five stages, and each one breaks down differently in production.

Ingestion is where bad chunking makes the system confidently quote the wrong paragraph. Embedding is where the wrong model treats "renew the policy" and "cancel the policy" as the same thing. Retrieval is where the right answer sits in the index while three worse ones surface first. Generation is where the model hallucinates if you let it. Evaluation is the stage most teams skip, and the one that decides whether the system holds up six months in.

We've debugged enough of them to know which stage to look at first when something goes wrong.

Where RAG fits

Where RAG earns its place.

RAG fits where the answer already exists in your data. You just need the system to find it, explain it, and prove where it came from.

For legal and compliance teams.

Your team re-reads the same 400-page document every time a question comes up. RAG answers from the clause that matters, with the citation and version attached.

For insurance and financial services.

Underwriting guidelines, product docs, and regulatory texts your staff and customers can actually query. Faster decisions, fewer mistakes, a clean audit trail.

For healthcare and pharma.

Clinicians searching protocols, drug information, and patient documentation without leaving their workflow. Grounded answers, traceable to the source, never invented.

For support teams handling deep documentation.

The obscure question buried on page 87 of a manual gets answered in seconds, in your voice, with the source attached. Your humans handle what actually needs a human.

For education and assessment platforms.

Match a learner's profile against a structured knowledge base — courses, eligibility criteria, qualifications. The system surfaces what fits them, not what's listed first.

For internal knowledge across the company.

Onboarding docs, SOPs, engineering runbooks, sales playbooks. The answer your team needs already exists somewhere. RAG makes it findable in one query, not five Slack threads.

How we build and scale.

Every RAG system we ship goes through four stages. Each one has eval gates before we move forward.

1

Discovery & data audit.

We map your knowledge base, collect real user queries, and build the eval set everything else gets measured against.

2

Pipeline design.

Chunking, embedding, vector store, retriever — chosen for your data and your queries, not for what's trending. You see the plan before we build.

3

Build, eval, harden.

We build the pipeline, tune until retrieval clears the bar, add guardrails for citations, scope, and low-confidence answers.

4

Production launch & continuous eval.

We ship behind a feature flag, monitor real traffic, and tune on real queries. Eval runs continuously, not just at launch.

Ready to see a RAG system grounded in your data?

Book a free 30-min call. We'll map your knowledge sources, your real user queries, and what your evaluation harness should look like. No pitch. Just the plan.

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