Data stays in your environment
Systems run in your cloud accounts under your access controls.
Approach
How an engagement runs.
Most AI consulting fails the same way: a system is delivered, the consultants leave, and nobody uses it. These steps are built against that.
Three steps
Working session
We review one process or technology cost with you and say whether a change is worth making. You keep the notes either way.
Process audit
We document the process as it actually runs, then design the system on paper: data flows, model choices, checkpoints. The result is a written audit document, and you review the design before anything is built. For technical cost reduction, the audit sets out the current spend, proposed changes, implementation cost, and expected savings.
Build & handover
We build inside your environment while your team is in the room, so training happens during the build. Documentation, runbooks, prompts, and admin access then transfer to your team. Ongoing support is available and never required.
Governance
Human checkpoints
Financial, legal, or customer-facing steps get an approval gate.
Audit trails
Every model input, output, and human correction is logged.
No training on your data
Model providers are configured with training disabled.
Grounded answers
Answers cite sources. “Not in the documents” is a valid answer.
Failure runbooks
Each system ships with who to page, how to pause it, and how work continues by hand.
Models
We are vendor-agnostic. We deploy on Anthropic Claude, OpenAI or Azure OpenAI, or open-weight models running privately in your infrastructure, depending on data residency, cost, and capability. Our reasoning is public: model routing and cost and how we evaluate.
See a sample audit.
A full audit document for a fictional company, so you can judge the work before you pay for it.
View the sample audit