First, we listen. Then, we're honest.
Most AI projects fail because nobody measured first. Our methodology starts with a fixed-scope audit of how your business actually runs — and ends every engagement the same way: with the honest verdict, including 'don't build this yet.'
The methodology
Audit-first, evidence-tagged, signed
A systematic approach that ensures every number we quote and every system we ship is ready for the real world.
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 progressive development with continuous testing, comprehensive end-to-end validation, and a scientific review gate — where our CSO rejects nearly half of implementations before production.
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.
The Numbers Behind the Methodology
In December 2025, we produced 76 agentic implementations totaling 214,000 lines of code. Only 37 passed our scientific validation gates—a 49% rejection rate.
This isn't inefficiency. It's quality control.
Every rejected agent taught us something. Every approved agent earned its place in production. The result: systems that perform reliably at scale — built by a team that learned its rigor in healthcare and bioinformatics, where wrong answers carry real consequences.
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?"
Every build ships with defined performance metrics established up front, validated against real-world scenarios before deployment — and measured again after. In December 2025 we built 76 agents; only 37 passed our validation gates. That 49% rejection rate is why production systems hold sub-1% error rates.
See the Methodology in Action
The AI Automation Audit is the methodology, productized: ten business days, fixed scope, findings you keep either way.