Generative AI solutions aimed at unstructured mess
The generative AI solutions that pay for themselves are the ones pointed at unstructured mess: PDFs, emails, recorded calls, and internal knowledge nobody can find. Turning that into something your existing systems can query is where the returns have been consistent.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Document extraction
Contracts, invoices, claims, and forms turned into structured data your systems can act on, including the scans that defeated your OCR project.
Enterprise search
Search across your internal knowledge that returns the right document, with citations that resolve to something a person can open.
Knowledge assistants
Assistants grounded in your policies and documentation, that decline rather than improvise when the retrieval comes back empty.
In-product copilots
AI inside your product that takes real actions, with permission checks so it cannot do what the logged-in user could not.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
Our documents are scanned and inconsistent. Does that work?
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Often better than expected, since native multimodal models read layout rather than running OCR first. The trade-off is provenance: you get the answer without always getting the exact source coordinates, which matters in regulated review.
How accurate is document extraction?
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Depends entirely on the document class, which is why we build against a random sample of your real documents including the bad ones. Any number quoted before that has been measured on someone else's data.
Can it work with sensitive documents?
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Yes, including self-hosted open weight models inside your infrastructure where nothing leaves the building.
How does this connect to our existing systems?
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Through a tool layer we build once, so the same connections serve the next project too. That is the difference between one solution and a capability.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.