An answer you can defend, not merely an answer.
Most compliance AI is probabilistic and hosted by the vendor. Both choices are defensible for a chatbot and disqualifying for a regulated file. Here is what we do instead, and what it costs us.
The model comes to your data. Your data never leaves.
Wecan deploys inside your own infrastructure or the sovereign cloud you choose. There is no outbound call to a public model API at inference time, which means no client name, no document and no beneficial-ownership chain crosses your perimeter to be processed by a third party.
- No public API calls
- Inference happens where your data already sits. Nothing is sent to a hyperscaler's model endpoint to be scored.
- No training on your data
- Your files never enter a training set — ours or anyone else's. This is a contractual commitment, not a setting you have to find.
- Your choice of ground
- Your own infrastructure, Swiss hosting, Luxembourg hosting, or on premise. The deployment target is your decision, not a tier in a price list.
Same file, same result. Today and in four years.
A probabilistic system regenerates an answer each time it is asked. That is acceptable when the cost of variation is nil. In a supervised file it is not: two analysts running the same check must not obtain two different verdicts, and an examiner reopening a 2026 decision in 2030 must find the reasoning that actually produced it.
- Reproducible output
- The same inputs produce the same result, every time. Not a plausible reconstruction — the same one.
- Traceable reasoning
- Every field carries its source; every alert closure carries its verdict, justification, timestamp and four-eyes sign-off.
- Consistent across officers
- The process, not the individual, determines the shape of the file. That is the finding supervisors look for, and the one no policy document can answer.
This is a harder way to build, and we chose it on purpose.
Calling a public model API is faster to ship and cheaper to run. Deploying into a client's environment means supporting their infrastructure, their upgrade cycle and their security review. We accept that because the alternative asks a compliance officer to defend, in front of their regulator, an output they cannot reproduce and a data flow they cannot describe. A model that is right most of the time is a promising experiment. A system a compliance officer can defend is something else.
See it run on your own files.
A live walkthrough on real KYC scenarios — no slides, no commitment.