The method, in full.
We're a new firm, so we don't ask you to take a track record on trust. We publish the engagement instead: what happens each week, what lands at each gate, how the result is measured, and what we do if the number doesn't move.
Thirty days, four gates.
One workflow, scoped narrow enough to finish. Every phase ends in something you can hold, not a status update.
Scope & baseline
One workflow, one metric, agreed in writing. We instrument the before number before a line of code is written, so the result has something to be measured against.
- Written scope with an explicit out-of-scope list
- The baseline metric, measured in your systems
- A named owner on both sides
Build on real data
Agents wired into your real systems of record inside a sandboxed environment. No demo dataset, no synthetic happy path, no slideware.
- Working system running in staging
- Evaluated against your data, not a sample
- A weekly demo you drive yourself
Harden
Guardrails, escalation paths, audit trail and failure modes. Where the agent stops and a human takes over is written down, not implied.
- Guardrail and escalation policy
- Full audit trail of every agent action
- Failure-mode review with your team
Ship & measure
Production deploy with monitoring, then the after number read against the day-one baseline. You get the delta whichever direction it points.
- Running in production
- A measured delta against the baseline
- Handover doc and runbook
The number is agreed before the work starts.
Most AI projects are judged on a demo. Ours are judged on a metric you picked, against a baseline you watched us take.
The metric is agreed on day one, in writing, before we build anything.
We instrument the baseline first, so the before number is yours rather than a figure we produce afterwards.
The dashboard lives in your stack. You read the number without asking us for it.
If the metric doesn't move, that's what the readout says. We don't re-frame the result.
It follows that we don't take engagements we can't measure.
We run our own delivery on the accelerators we sell.
Forge sandboxes every coding agent we use, Studio scaffolds internal tools, and Atlas is the context layer underneath. You are not the first user of any of them.
Forge
The framework we use to run frontier coding agents like Claude Code inside a sandboxed, isolated environment, keeping data in place and every action on an audit trail.
Studio
An accelerator for standing up internal tools and workflows fast: describe the app, and we generate and deploy it with enterprise controls and governance in place.
Atlas
Connects systems, metrics and dependencies into one navigable graph that agents and dashboards can reason over: the shared context layer behind our ops and finance work.
AI-native, with the guardrails written down.
Agents do a large share of the work inside our own delivery. These are the rules that keep that honest.
Coding agents, sandboxed
Frontier coding agents run inside Forge: isolated execution, with every prompt and every diff on an audit trail.
Humans approve every merge
Agents write and agents review, but they don't self-approve. A person owns the merge and the consequences.
Tests and evals gate the merge
Not the demo. A change that can't be evaluated doesn't ship, however good it looks in a walkthrough.
Your repo, your cloud
We work where your data already lives. Zero data movement by default, and access scoped to the job.
What that way of working buys us on our own delivery.
Pick the workflow. We'll scope the thirty days.
Book a discovery call and we'll map one workflow worth re-engineering, the metric it moves, and what the baseline looks like.