Grounded, trustworthy AI that cites its sources, passes audit, and actually reaches production. For companies starting out, and for those whose AI works in a demo and dies before it goes live.
Twenty-seven production systems shipped, most of them where shipping is hardest.
Six kinds of work we put agents on. Each page names what gets built, the systems on the work page that prove it, and how it stays trustworthy.
Three ways to find out what is actually stopping your AI from paying off, before anyone talks about a budget. No sign up, and they run on hardware in this studio rather than on somebody else's API.
The five phases every company climbs before AI pays off, in order, with the three signals that tell you which one you are on, the four moves that close it, and what skipping it costs. One page per phase.
Fourteen questions at most, and it stops as soon as another one would not change the answer. It names the most likely cause, the problem travelling with it, where you sit on the roadmap, and what the symptoms are costing you each year. You leave with a report you can forward.
Describe what is not working with your data or your AI, in your own words. It records only what your words support, asks the one question that separates eight competing explanations most, and names none of them until there is enough evidence. Then it shows you the evidence against its own conclusion.
One conversation, eight competing explanations held back until there is enough evidence. It records only what your words support, asks the question that separates the explanations most, and shows the evidence against its own conclusion. Fourteen questions at most. Nothing you type is stored, only the readings.
This page used to be that catalogue. It has its own home now, so each build can be read for the architectural decision that makes it work where a general purpose assistant would not.
Travis Dayton
Data architect and AI engineer. Based in Barcelona.
Travis Dayton has spent more than fifteen years building and governing data systems inside regulated industries, and now builds agentic AI that can be trusted inside those same environments.
His career has been shaped by sectors where a wrong answer carries consequence. At Bayer he established enterprise data governance across pharmaceutical research, regulatory affairs and commercial domains under 21 CFR Part 11 and global GxP. At Gulfstream Aerospace he delivered under ITAR and DFARS, where the nationality and location of the person touching the data is itself a legal control. Before that he authored data governance policy at Portland General Electric under NERC and FERC, and at Andor Health he built governed pipelines for a platform carrying clinical data under HIPAA. GDPR runs across all of it.
That background is why the AI here looks different. These systems cite their sources, refuse rather than invent, and stop to ask a person when the ground is uncertain. They ship as LangGraph applications through one deployment pipeline, with production inference running on owned hardware, so client data does not have to leave the perimeter.
Requirements here are not a document that goes stale beside the code. They are the program. The specification is itself a LangGraph: every requirement is a node, decomposed until a leaf names exactly one function that has to exist, and agents build against that graph rather than against a brief.
That is what a LangGraph specification is. The requirements, the order they run in, and the test that proves each one are a single executable graph, so the specification can be run, and can fail, in the same way the software can.
A requirement stays a parent until it can name exactly one implementable function. The tree keeps going down until there is nothing left to interpret.
A second relation records what must have run first at runtime. The tree says what the thing is made of. The graph says what happens when. They are deliberately not the same relation.
Each leaf resolves through an implementation registry to a callable function. Correspondence is a property of the code, not a claim in a table, so the specification cannot quietly drift away from the build.
Every leaf carries assertions that call the function and check the behaviour the requirement asks for. A leaf with no assertion fails. You cannot add a requirement without saying how you would know it works.
Once the LangGraph specification is machine readable and gated, the build is handed to agents. They have somewhere exact to aim, and the gate is what tells them, and me, the moment they are wrong.
Yes, and no. Fourteen questions at most, about six minutes, no account and no email address required. The report is yours to download at the end, and you can forward it inside your company without asking anyone here for permission.
The most likely cause of the stall with a probability against it, the problem travelling with it, which of the five roadmap phases you are on, and a band for what the symptoms are costing you each year with the arithmetic printed so your finance officer can check it. It says plainly that it is a hypothesis built from what you reported, because a document that overclaims is worse than no document.
Into this studio's own warehouse and nowhere else. The models run on hardware here rather than a third party API, and what is stored is the answer you selected rather than the words you typed.
Yes. The problem is an AI system that cannot get through an internal control gate, and that gate is a gate whether it is called validation, security review, procurement or legal. The regime differs between HIPAA and GxP and a purchasing committee. The work that gets you through it does not.
Because the later work does not hold without the earlier. You cannot agree what a customer is across systems you cannot search, and you cannot pass a security review on a system whose data flows nobody has written down. A phase 0 gap at twelve percent outranks a phase 4 gap at seventy for that reason, which is also why doing the phases out of order feels like progress and produces none.
Nothing automatic, and nothing lands in a sequence. If you send the enquiry, a person reads it and answers, and your finding is carried into the form so the conversation starts from the phase you are actually on.
If your institution, your operation, or your obsession deserves software this grounded, write. Tell us the world it lives in and you will get back something real: what it would take, what it would cost, and what it would do on day one.
Not sure what to ask for yet? That is the normal place to start. Run the free diagnostic first: it names the problem, puts you on the roadmap, and brings the answer back into this form for you.
