Read the documents. Map them against a dated rulebook. Return findings that quote the passage behind each one. What we remove is the reading, and the days of assembling proof afterwards.
Shipping inside a customer's own environment is not new to us. It is how the last several systems were delivered — behind network isolation and private endpoints, through the client's own architecture review board.
Two founders, both working directly on every engagement.
An economist who moved into applied deep learning. Doctoral research at Inland Norway University of Applied Sciences, published in IEEE Access, after earlier work on macroeconomic modelling and financial markets. She owns the measurement half of the product — the evaluation sets every rule pack is scored against, which is the part most AI companies do not staff at all.
AI engineers and architects, with experience from Fortune 500 enterprises and large-scale life sciences delivery. Google Cloud Certified Professional ML Engineer. Seven years in applied AI, four of them in industrial doctoral research. Agentic systems hand-coded to run inside customers' own cloud tenancies, delivered as architecture lead of a team of five engineers.
Every few months a new model reads technical documents better than the last. That capability is not scarce, not defensible, and within a release or two it will be free. Building a company on reading slightly better than a general assistant is building on somebody else's roadmap.
What does not arrive with the next model is the ability to prove how a decision was made. A regulated decision has a different shape from a good answer. Reproducible — the same document, the same rulebook, the same finding in three years. Traceable to the dated version of the rule in force that day. Quoted rather than paraphrased. And signed by someone who can be named when an inspector asks.
So the work that survives is unglamorous. Rule packs with dates attached. Evaluation sets labelled by an organisation's own experts. Validation documentation generated by the release. None of that is intelligence. All of it is labour — and labour is not what model releases erode.
Model releases commoditise intelligence. They do not commoditise evidence.
What good looks like, which criteria are critical, and what counts as failure — agreed in writing before any work starts, not argued about at the end.
We measure how long the review takes you today and how often your own reviewers agree with each other. Most organisations have never measured either. The rule pack is built against that.
It runs on your real documents and is scored against your experts' labelled decisions. You end the four weeks with measured numbers, a working prototype and a decision you can defend — not an open-ended programme.
If a review in your organisation takes an expert hours per document and has to be defensible afterwards, that is the conversation worth having.
Book a callOr write to hello@hridhaan.ai.