We build retrieval systems that answer from your own documents and show the passage behind every claim, so your team can check the work instead of trusting it. And when the answer is not in your material, the system says so.
Thirty minutes with the engineer who builds these systems. No slide deck.
A general model does not know your policies, your contracts or your guidelines. When it does not know, it rarely says so.
Each system is built around your documents, your users and your security requirements. Start with one knowledge base or connect several across the operation.
Policies, contracts, manuals and past files turned into material your team can question in plain language instead of hunting through folders.
Mixed formats, awkward layouts and scanned pages processed and structured so the system can find the right passage rather than the roughly right document.
Semantic retrieval finds the passages that actually answer what was asked, even when nobody knows the exact phrasing the document used.
Every answer carries the passages it was built from, so verifying takes a click. This is the difference between an interesting demo and something people will rely on.
Built so sensitive content is not exposed to public tools, with access controls that mirror the permissions your team already has.
We build a test set from real questions and measure against it before launch, then keep checking as documents change and the knowledge base grows.
The model never gets to answer from memory. It answers from what was actually found in your documents, and you get to see it.
Your documents taken in, whatever format they arrive in.
SystemSplit and indexed so a specific passage is findable, not just a file.
SystemThe passages that answer the question, inside the asker's permissions.
SystemThe answer is built only on what was retrieved, not on general knowledge.
SystemIf your documents do not cover it, the system says so instead of guessing.
SystemThe source sits beside the answer. The last judgment is yours.
Your teamStanford researchers ran the first preregistered evaluation of commercial RAG legal research tools and found the providers' claims overstated. The purpose-built tools hallucinated between 17% and 33% of the time, against 43% for a general purpose model on the same queries. Grounding made a real difference. It did not make the problem go away, and marketing that says otherwise is setting your team up to stop checking.
So we design for the failure case rather than around it. Every claim carries its passage, so a wrong answer is one click from being caught. The system is built to say it does not know rather than fill the gap. And before launch we measure against a test set of real questions your team asks, so you see the accuracy number before you rely on it instead of after.
A fabricated citation is the easy failure, because it is detectable. The dangerous one is a real document, correctly named, described as saying something it does not say.
We build the knowledge base from the systems your material sits in today, including in house repositories.
Ingestion quality varies by document type and condition. Scanned and handwritten material is harder than clean text, and we say plainly what is workable during the audit.
We learn what your team needs to find and how they need to trust it, then build in stages you can inspect.
The questions your team keeps asking, and the documents that hold the answers.
Your documents, users, accuracy bar and privacy requirements, with the coverage defined.
Built on your content and tuned so answers land on the right passages, tested against real questions.
Placed where your team works, with access controls set, then re-evaluated as content changes.
RAG connects an AI model to your own documents so it answers from your trusted content rather than general knowledge. Before it responds, it retrieves the relevant passages from your material and builds the answer on what it found. A public tool does not know your policies or contracts, and it will not show you which of your documents an answer came from.
Yes, and anyone who tells you otherwise is selling you something. Stanford researchers found that purpose-built RAG legal research tools still produced misleading or false answers between 17% and 33% of the time, against 43% for a general purpose model on the same queries. Grounding reduces the problem substantially. It does not remove it. That is exactly why we put the source passage next to every answer, so a wrong answer is one click from being caught rather than quietly relied on.
The system says so rather than filling the gap. A partial answer is marked as partial, and the part it could not support is called out instead of being smoothed over. Refusing to answer is a design goal, not a failure.
Your documents are not exposed to public tools, and your data is never used to train any model. Access controls mirror the permissions your team already has, so someone asking a question only ever gets answers from material they were entitled to read. We agree the handling and retention rules in writing before the build starts.
We agree a target accuracy with you during the audit, then build a test set from real questions your team actually asks and measure against it before anyone relies on the system. You see those numbers before rollout, not after. We keep running that evaluation as your documents change.
A build fee for the first knowledge base plus a monthly fee to run, host and maintain it. You get a firm number after the workflow audit, before you commit to anything.
Bring the questions your team asks most and tell us where the documents live. We will show you what a grounded answer would look like, and say plainly where your content is not ready.
