AI Proving Ground
Prove the AI before you trust it.
Our practice for putting AI inside your systems correctly - governed, evaluated, and secured before it touches production.
The problem
Companies don’t fail at the model.
They fail at everything around it - the data feeding it, the controls watching it, the integration carrying it.
60%
of AI projects are abandoned - built on data that was never AI-ready.
52%
of answers on ungoverned data are fabricated. Same model, no governance.
~7%
of companies get AI past the demo and into production.
The insight
It’s the system around the model.
Governed data
Grounded retrieval
Evaluation
Beats a baseline
Security
Hardened access
Monitoring
Drift & hallucination
We build the layers around the model - and prove each one before production.
The method
We build it, then prove it.
Readiness audit
We assess your data, systems, and the specific task before any model is chosen. You learn where AI will hold and where it will not - in writing, before you spend on a build.
Build
We build the system around the model - governed retrieval, evaluation against a baseline, and security-by-design. Nothing reaches your users on a promise.
Run
We monitor the system in production for hallucination, drift, and regression, and improve it on evidence. Proving does not end at launch.
The proof
Proven, not promised.
Every release is gated, guarded, and logged - evaluation, security, and monitoring, in the open.