First, we listen. Then, we're honest.
Our methodology starts with a fixed-scope audit of how your business actually runs and ends with a documented recommendation, including when to defer a build.
The methodology
Audit-first, evidence-tagged, signed
A structured way to connect recommendations, test results, and deployment decisions to evidence.
Listen First
Every engagement starts with the people who do the work — up to six stakeholder interviews and read-only walkthroughs of the systems you already own. We sit with your team before we propose anything.
Measure, Don't Assert
We replay an automation agent in shadow mode against your historical data, scored against the outcomes your people actually produced. A backtest, not a mock — every claim is a query against a database.
Tag the Evidence
Every finding cites its source — [I-2] is interview two, [A-4] is artifact four. Every return-on-investment figure is recomputable from the methodology appendix. Your finance chief can check our work.
Deliver the Honest Verdict
Including what not to build. If the data-quality snapshot scores low, the report leads with 'fix your plumbing first.' If an opportunity doesn't pay back, we decline it in writing. The willingness to say no is policy, not personality.
Build With Validation Gates
Approved builds go through staged development, testing, and scientific review against acceptance criteria agreed for the engagement. Unresolved findings stay visible in the release decision.
Sign the Work
Every findings report is reviewed and signed by a PhD scientist. Every build ships with defined performance metrics validated against real-world scenarios, not happy paths.
What We Check Before Release
Each build needs test cases that reflect its intended use, including failure paths and conditions where a person must review the output.
Acceptance criteria make the release decision reviewable.
We document the data used, observed results, unresolved issues, and operating limits. Those records support a client's decision to release, revise, or stop a build.
Addressing the Hard Questions
Questions we hear from owners and CFOs — and our honest answers.
"Why is the audit paid? Everyone else does discovery for free."
Free discovery is a sales call with a questionnaire attached — its job is to close you, not inform you. The paid audit ends in deliverables you keep either way: findings, return-on-investment models, an opportunity map, and working software built on your data. And if you proceed within 60 days, the fee credits in full against the build — so proceeding makes it effectively free.
"What if you find nothing worth building?"
Then the report says so, and you've spent $10,000 to avoid a six-figure mistake. Negative findings, delivered — that's the job. We compute returns from your data; we don't promise them, and we don't invent them.
"Do we need an information-technology department for this?"
No. The audit rides on systems you already own — QuickBooks, Microsoft 365, your customer-management system, your spreadsheets. We need read-only access and a few hours of your team's time, not a technology project.
"How do we know your agents actually perform?"
We agree on performance measures and representative test cases before building, then document the results and limitations before a deployment decision. Expected performance depends on the task, data, and operating conditions; it must be measured for your use case.
See the Methodology in Action
The AI Automation Audit is the methodology, productized: ten business days, fixed scope, findings you keep either way.