AI grounded in
your own data.

Not on the configurations we deploy. We use enterprise API tiers where inputs are excluded from training, and we document exactly what is sent to which provider. Where the data is too sensitive to leave your environment, we can run open models on infrastructure you control.

Retrieval grounding so answers come from your documents, citations so any claim can be checked, an evaluation set that catches regressions, refusal behaviour when confidence is low, and human review on consequential outputs. Not eliminated — measured, bounded and monitored.

We model cost per request during the pilot and give you a projected monthly figure at expected volume, with a hard cap and alerting. For most business workloads the running cost is far smaller than clients expect — the build is the significant number.

It is the normal starting position. Data preparation is usually the largest part of the work, and we scope it explicitly rather than discovering it halfway through. What matters is that the information exists somewhere, even messily.

Yes — CRM, helpdesk, ERP, document stores, Slack, Teams and email are all routine. The best AI systems appear inside the tools people already have open rather than as another tab to remember.