Data stays in your environment
- Systems run in your cloud accounts under your access controls. We work inside your environment; your data does not move into ours.
Approach
The method carries the weight.
Every engagement follows the same five phases, ends the same way — with your team running the system — and carries the same governance practices. This page is the whole method. There is nothing behind it we'd only show you after a signature.
Five phases
Process mapping
We sit with the people who do the work and document the process as it actually runs — volumes, exceptions, workarounds. Most automation failures start with automating the process as management imagines it.
Architecture design
We design the system on paper first: data flows, model choices, checkpoints, failure handling. You review the design before a line is built. The audit document is where this lives.
Build & integration
We build inside your infrastructure and your access controls, integrated with the systems you already run. No parallel stack you have to maintain, no data leaving your environment for ours.
Team enablement
The people who will operate the system are in the room while it's built. Training is not a session at the end; it's how the build is run.
Handover
Documentation, runbooks, prompts, and admin access transfer to your team. The engagement ends when they operate the system without us. Ongoing support is available, but never required.
Working principles
Most AI consulting fails the same way: a system gets delivered, the consultants leave, and six months later nobody uses it.
Everything about how we work is arranged against that outcome. It's why adoption is scoped into the build, why documentation is complete, and why handover — not renewal — is the goal of every engagement.
Adoption is scoped in
A system your team doesn't use is a failed project. Training and rollout are part of every build, not an add-on.
Everything is documented
Architecture, prompts, runbooks. You get all of it. There is nothing we could hold back to keep you dependent.
Handover is the goal
Engagements end with your team operating the system on their own. We measure ourselves against that.
Governance, built in
These are engineering practices in every system we build, not aspirations. Because systems are built inside your environment, they inherit your existing controls — and we design to the expectations of SOC 2 and GDPR review from the first architecture document.
Human checkpoints where it counts
- Any step with financial, legal, or customer-facing consequence gets a human approval gate. Full automation is a choice you make deliberately, not a default.
Audit trails on everything
- Every model input, output, and human correction is logged. When someone asks "why did the system do that," there is an answer.
No training on your data
- Model providers are configured with training disabled. Your documents inform answers at runtime; they don't become anyone's training set.
Grounded answers only
- Knowledge systems cite sources, and "not in the documents" is a valid answer. We would rather return nothing than something plausible and wrong.
Failure is planned for
- Every system ships with a runbook covering what to do when it misbehaves — who to page, how to pause it, and how work continues manually in the meantime.
On models
We are vendor-agnostic. Depending on your requirements — data residency, cost profile, capability — we deploy on Anthropic Claude, OpenAI or Azure OpenAI, or open-weight models running privately in your infrastructure. The architecture is designed so the model is a component, not a commitment: when a better or cheaper model appears, you swap it without rebuilding the system.
Our reasoning on model selection is public — see our decision framework and when fine-tuning is and isn't worth it.
Read the sample audit next.
The audit document is the clearest picture of how we think. We publish a full sample so you can judge the work before you pay for it.
View the sample audit