YOUR PROBLEM
The auditor asks "Why was this denied?" What do you say?
Regulators want to know how your systems make decisions. GDPR Article 22. EU AI Act. SR 11-7. Fair lending laws. They all require the same thing: explain your automated decisions.
But most systems weren't built to explain themselves.
THE COMPLIANCE CRISIS
340%
Increase in AI-related litigation
2022-2024
€35M
EU AI Act penalties
Or 7% of global turnover
72 hrs
GDPR SAR response window
Including decision explanations
$98M
Navy Federal fair lending settlement
2024
WHY CURRENT SYSTEMS FAIL
Logging isn't explaining
You have logs. Terabytes of them. But "user_id=12345, action=DENY, timestamp=..." doesn't answer "why." You need the reasoning, not just the outcome.
Code archaeology takes too long
To explain a decision, someone has to trace through the code, understand the logic, reconstruct the state at decision time. That's days or weeks per request.
Documentation drifts from reality
The spec says one thing. The code does another. Which one do you explain to the auditor? Neither answer is good.
ML models are black boxes
"The model said deny" isn't acceptable to regulators. SHAP values and feature importance don't satisfy legal requirements for "meaningful explanation."
THE INSIGHT
What if systems explained themselves?
The problem isn't generating explanations after the fact. The problem is that systems aren't built to be explainable.
There's a different approach: build systems where every decision is inherently traceable to the specification that produced it. Not reconstructed. Not approximated. Directly traceable.
SEE IT IN ACTION
Watch an auditor query get answered in seconds—with complete decision trace.
WHAT THIS MEANS FOR YOU
Instant audit responses
Any decision, any time, complete trace. No code archaeology. No reconstruction.
GDPR Article 22 compliance
Right to explanation? Every automated decision is explainable by design.
EU AI Act ready
High-risk AI documentation requirements? Generated automatically from specifications.
Fair lending defensibility
Prove your decisions are based on legitimate factors. Mathematical proof, not statistical sampling.
REGULATIONS WE HELP WITH
GDPR Article 22
Right to explanation for automated decisions
EU AI Act
High-risk AI documentation requirements
SR 11-7
Model risk management (banking)
ECOA / Fair Housing
Fair lending and disparate impact
SOC 2
Processing integrity and change management
Industry-specific
HIPAA, PCI-DSS, state regulations
AND THIS IS JUST ONE APPLICATION
The same technology that does this also...
Accelerates releases
Impact analysis in seconds. Ship weekly instead of quarterly.
Makes AI controllable
AI writes specs, machines verify. Auditable by design.
Extracts legacy knowledge
Understand old systems. Make them explainable.
Replaces tests with proofs
Mathematical certainty. Not statistical confidence.