About CleanAim®
33 years of learning systems. 8 patents pending. 1.1 million lines of proof.
CleanAim® builds infrastructure that makes AI systems provably compliant, continuously improving, and portable across providers. We're solving the same problem we've been solving since 1993: how do you capture decisions, pair them with outcomes, and enable compound improvement?
THE STORY
Systems that learn from outcomes always outperform systems that optimize once
CleanAim®'s architecture originated from a simple insight that has driven four businesses over three decades.
The architecture evolved, but the core principle remained: capture decisions, pair with outcomes, enable compound improvement.
THE INSIGHT
Optimization and compliance are the same problem
When large language models emerged, we recognized the opportunity: the same architecture that enables continuous improvement also generates the audit trail that compliance requires.
A system that learns from outcomes naturally produces the evidence regulators need.
Most governance only observes AI. CleanAim® learns from it.
THE DISCOVERY
We pointed it at ourselves. What we found changed everything.
In 2026, we turned our own verification methodology on ourselves. We had 1.1 million lines of AI-assisted production code. Health checks said HEALTHY. Tests passed. CI was green.
Then we built a behavioral verification system — Silent Wiring — that doesn't just check if code compiles or passes tests. It checks whether data actually flows through every declared path, whether functions produce real outputs, and whether integrations do what they claim.
We found 3 completely silent data pipelines — code that was wired together perfectly but moved no data. 82 runtime violations. A calibration engine returning hardcoded defaults for months. None of this showed up in tests, monitoring, or code review.
That discovery became our product. Silent Wiring verifies that AI-generated code actually works — not just that it exists.
THE PROOF
We didn't just design it. We built it.
1.1 million lines of production code, all governed by the same framework we offer to customers. The system is honest enough to say "insufficient evidence" rather than certify what it can't verify.
Lines of production code
Audit score—2 checks await calibration data
Test functions
Blockers—zero architectural violations
FOUNDER
Tom Killi
Founder & CEO
Tom Killi has built systems that learn from the prediction-outcome loop since 1993.
In 1996, he founded x.hlp Technologies, which developed capture-pair-learn methodology for enterprise training. The company served 30+ Fortune 500 clients—JP Morgan, Shell, McKinsey, NATO—before acquisition in 2004. That technology now operates inside SAP.
His prior patent work has been cited by 52+ patents from Microsoft, Apple, Samsung, IBM, and SAP over 25 years.
Patents
Patent Citations
Years in Learning Systems
MISSION
Making AI trustworthy
To build infrastructure that makes AI trustworthy—not by limiting what AI can do, but by ensuring that what it does can be understood, verified, and improved.
VALUES
How we build
Four principles guide everything we create.
Constitutional
We build systems that enforce their own rules. Not guidelines that can be ignored, but architectural constraints that cannot be bypassed.
Honest
Our systems admit when they don't know. A 98/100 score with explanation is more trustworthy than a 100/100 score that papers over gaps.
Independent
Provider tools assess provider systems. We build tools that assess any system from any provider—true independence by architecture.
Compound
One-time optimization is valuable. Continuous improvement is transformational. We build systems that get better every day.
COMPANY
Company facts
Get in touch
Whether you're interested in the CleanAim® Platform, partnership opportunities, or just want to learn more.
hello@cleanaim.com