An answer you cannot trace is just a guess.
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.
Which questions does your team keep asking, and where do the answers actually live?
Thirty minutes with the engineer who builds these systems. No slide deck.
Six ways the confident answer costs you.
A general model does not know your policies, your contracts or your guidelines. When it does not know, it rarely says so.
- 01 An answer that sounds right and cites a document you do not have
- 02 A policy quoted from a version you replaced two years ago
- 03 A real document, accurately named, describing something it does not say
- 04 The one exception that matters, quietly left out of the summary
- 05 Sensitive material pasted into a public tool to get a quick answer
- 06 Nobody able to reconstruct where an answer came from a month later
What goes into an XtractSol RAG system
Each system is built around your documents, your users and your security requirements. Start with one knowledge base or connect several across the operation.
Scattered documents, one place to ask
Policies, contracts, manuals and past files turned into material your team can question in plain language instead of hunting through folders.
Real documents, not clean ones
Mixed formats, awkward layouts and scanned pages processed and structured so the system can find the right passage rather than the roughly right document.
Understands the question, not just the words
Semantic retrieval finds the passages that actually answer what was asked, even when nobody knows the exact phrasing the document used.
The source sits next to the answer
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.
Your documents stay yours
Built so sensitive content is not exposed to public tools, with access controls that mirror the permissions your team already has.
Measured before anyone relies on it
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.
An answer, and the sentences it was built from
A cited answer is only useful if the citation goes somewhere. A document name proves nothing on its own, because the hard failure is not an invented source. It is a real source, correctly named, described as saying something it does not say.
So each claim carries the passage behind it, not just a reference. Checking becomes reading one sentence rather than reopening a policy manual.
The third clause is the important one. The fee was asked for and the documents do not contain it, so the system leaves it unanswered instead of producing a plausible number.
Yes, subject to an affidavit and underwriter sign-off.1 Where the servicer has been dissolved, the file follows the extended review path rather than the standard one.2 The fee for this path is not stated in your documents.?
Insurance over an unreleased instrument requires a recorded affidavit of loss together with written sign-off from a senior underwriter.
Where the original servicer is dissolved or otherwise unreachable, escalate to the extended review path before issuing.
Nothing in the indexed material covers pricing for this path. The system reports the gap rather than estimating.
Retrieve first, answer second, cite always.
The model never gets to answer from memory. It answers from what was actually found in your documents, and you get to see it.
Ingest
Your documents taken in, whatever format they arrive in.
SystemStructure
Split and indexed so a specific passage is findable, not just a file.
SystemRetrieve
The passages that answer the question, inside the asker's permissions.
SystemGround
The answer is built only on what was retrieved, not on general knowledge.
SystemAdmit
If your documents do not cover it, the system says so instead of guessing.
SystemVerify
The source sits beside the answer. The last judgment is yours.
Your teamSeveral vendors have promised hallucination-free. The research did not agree
Stanford 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.
Wherever your documents already live
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.
You see the answer quality before you roll it out
We learn what your team needs to find and how they need to trust it, then build in stages you can inspect.
Discovery call
The questions your team keeps asking, and the documents that hold the answers.
Workflow audit
Your documents, users, accuracy bar and privacy requirements, with the coverage defined.
Build and tune
Built on your content and tuned so answers land on the right passages, tested against real questions.
Deploy and improve
Placed where your team works, with access controls set, then re-evaluated as content changes.
What is RAG, and why not just use a public AI tool?
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.
Can it still get things wrong?
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.
What happens when the answer is not in our documents?
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.
Does our data stay private, and will it train any model?
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.
How do you know it is good enough to launch?
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.
What does it cost?
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.
Ready to get started?
Book a free discovery call and we will map how agentic AI can fit your workflows.