Why we built Veydria
Most teams can ship an AI feature in a week. Writing down what it does, working out which rules apply, and proving it behaves takes far longer. That work usually happens last, in a hurry, right before a customer or a regulator asks. We built Veydria to close that gap.
The problem
AI went from demos to production faster than the paperwork could keep up. A model gets swapped, a prompt changes, a new agent goes live, and the risk assessment someone wrote three months ago is already wrong. Compliance done by hand in documents drifts away from the system it is supposed to describe. When an auditor shows up, teams end up reconstructing what happened from logs, chat threads, and memory.
The approach
Veydria runs one workflow with five steps: discover, classify, evaluate, monitor, and prove. You connect your systems once. The rules engine maps each one to the obligations that apply. Evaluations score agents for the failures that matter. Production activity streams into an audit log you can verify. The documents auditors ask for are generated from that same live data, not typed up on the side. Because every step reads from the one before it, the record stays current without a person keeping it in sync.
What we believe
Three ideas shape the product.
Evidence over promises
A claim you cannot show is not worth much. Everything in the product ties back to data you can open and check for yourself.
Keep it in your database
Your systems, results, and audit trail live in your own Postgres, embeddings included. There is no separate vector store to run and secure.
Documents that read like a person wrote them
An Annex IV file should be clear to the engineer who owns the system and to the auditor reading it. We aim for plain writing, not a wall of filler.
Who we are
We are a small team that has shipped AI inside regulated products and felt this pain first hand. We would rather build one honest workflow than a pile of dashboards nobody trusts. If that sounds like a tool you want, we would like to hear from you.