The methodology
Models aren't the constraint.
Your company is.
The best AI tools in the world still hit the same wall: a company built for humans, not agents. We find that wall, redesign what sits behind it, and build the shared brain your people and your agents both work from. Then the good work starts to compound.
Three stages
From AI-assisted to AI-native. In order, not all at once.
Each stage sets up the next. Skip one and the skip shows up later, usually as a pilot that quietly dies.
Find where the good work is stuck.
We score how ready each team actually is, watch the real work rather than a survey of it, and name the one thing holding it back. You leave with a map your leadership can act on, not a sixty-page readiness report.
Make the company legible.
Roles get broken into the jobs inside them, then sorted: what an agent can take, what stays human, what to retire. The rules and judgment your best people carry in their heads get written down. We choose the agents and tools that fit your stack and test them on real work before you commit to anything.
Put agents to work, keep people on judgment.
Agents go live with clear mandates and supervision. Structured work routes to them; judgment, relationships, and growth stay with your people. We run workshops until the team is genuinely fluent, not just trained. Every project deposits what it learned back into the shared brain, so the next one is easier.
The six vectors
We score six things. The shape is the diagnosis.
Each scores one to twelve. Your real capability is capped by your weakest critical vector, not your strongest. That weak one is usually where the good work is stuck.
Process legibility · the spine
How much of the real work, exceptions and tribal knowledge included, is written down and findable. Most companies overrate themselves here by a full tier.
Data architecture · the floor
How clean and connected your data is across systems. Scattered silos at one end, a real-time substrate agents can read and write to at the other.
AI adoption · the visible one
From nobody using it to genuine organizational fluency. Easy to start anywhere, fragile until the rest of the company catches up.
Organizational design · the shape question
If you rebuilt this team today, knowing what agents can do, who would you hire? How far is that from what you have now?
Cultural readiness · the one that kills pilots
The willingness to change how work gets done. It beats every technical gap as a cause of failure. You can have great docs, clean data, and still lose here.
Governance · the steering, not the brake
What AI may and may not do, decided in advance. The companies that move fastest are the ones that made those calls early.
The binding constraint
Your capability is capped by the weakest link, not the strongest.
Most companies invest in their strengths, because strengths are visible. The weak vector quietly drags everything down, and it is harder to see and harder to fund. It is also the thing keeping your work from getting dramatically better. Our job is to find it and name it.
Our working bet: it usually lives in one of three places. Data architecture, governance, or culture. So we sequence the same way every time. Fix the data first, because it blocks everything. Formalize governance second, the moment AI starts touching real work. Then process legibility and org design together.
From method to audit
The methodology is the instrument.
The audit is where you use it.
Four weeks. Six vectors. The one constraint holding your team back, named and sequenced, with a plan your leadership can act on.