The Complete Platform
Constitutional Learning Infrastructure for Enterprise AI
CleanAim® is a unified architecture that powers both AI Code Audit (behavioral verification for engineering teams) and AI Governance (EU AI Act compliance infrastructure). One platform. Two pillars. Proven across 1.1 million lines of production code.
Intelligence Ownership
Your data, patterns, and calibration stay yours
Competitive advantage from institutional learning
Intelligent Orchestration
Smart routing across multiple AI providers
Better quality, lower cost, vendor flexibility
Complete Portability
Learning transfers across models and clouds
Escape vendor lock-in, future-proof investments
THE ARCHITECTURE
One governance layer. Every provider. Every use case.
Constitutional Capture
Every AI prediction is paired with its outcome. Not behavioral logging (62-76% capture), constitutional capture at the infrastructure layer (99.7% capture).
Learning That Compounds
The system improves based on what actually happens. 78.3% MAE reduction over time. Learning that can't be accidentally deleted.
Provider Independence
One governance layer works across all providers. 93.3% transfer efficiency (internal testing across Claude, GPT, Gemini and Grok; 250 tasks) when switching between Claude, GPT, Gemini, and Grok.
Immutable Audit Trail
Event-sourced architecture where every state change is recorded. Deterministic replay for any decision.
TWO PRODUCTS
One platform. Two pillars.
AI Code Audit
Engineering teams
Behavioral verification for AI-generated code
Proves code actually works, not just compiles
Learn about AI Code Audit →AI Governance
Compliance teams
Continuous AI governance infrastructure
EU AI Act compliance that's architectural, not theatrical
Explore AI Governance →PLATFORM CAPABILITIES
Eight core capabilities
Automatic Learning Capture
Every AI prediction and its real-world outcome are automatically recorded and paired, creating a closed loop where the system learns from experience.
EU AI Act Article 12 requires automatic logging of all AI decisions.
Doubt Engine
Before any AI decision executes, CleanAim® calculates a "doubt score" indicating how likely the AI is to be wrong. High-doubt decisions are automatically routed for review by a person.
Article 14 requires people to understand AI limitations.
Human Oversight Orchestration
A complete system for routing AI decisions to qualified people, tracking their reviews, and proving they actually engaged, not just clicked "approve."
Proves "appropriate competence" for Article 14.
Automation Bias Detection
Monitoring that detects when reviewers are "rubber-stamping", approving AI decisions without meaningful engagement.
Article 14 requires people to "remain aware of automation bias."
Decision Velocity Monitoring
Tracking the rate at which AI makes decisions compared to review by a person capacity, with alerts when throughput exceeds what people can meaningfully oversee.
Multi-Provider Orchestration
Unified management of multiple AI providers through a single interface, with intelligent routing based on cost, quality, and availability.
Provider independence ensures audit integrity.
Cross-Model Learning Transfer
Everything CleanAim® learns while working with one AI provider applies when switching to another. Cloud exit insurance.
Counterfactual Explanations
Automatically generated explanations of why an AI made a specific decision, including what would have changed the outcome.
GDPR Article 22 and EU AI Act require explainable automated decisions.
INTELLECTUAL PROPERTY
8 pending patents
| Patent | Innovation | Key Capability |
|---|---|---|
| PAT-001 | Constitutional Capture | 99.7% pairing in patent validation (vs 62-76% prior art) |
| PAT-002 | Self-Calibrating Doubt Engine | 78.3% MAE reduction |
| PAT-003 | Cross-Model Transfer | 93.3% Claude→GPT/Gemini/Grok (internal testing, 250 tasks) |
| PAT-004 | Event-Sourced Audit | FDA/SOX ready |
| PAT-005 | Genetic Pattern Evolution | 14-42x faster than standard GA |
| PAT-006 | Self-Diagnosing Audit | System monitors itself |
| PAT-007 | Multi-Model Consensus | Evolving weights, no retraining |
| PAT-008 | External Safety Coordination | <5 sec full cross-provider stop |
COMPLIANCE MODULES
Ready-built compliance frameworks
EU AI Act
Articles 9, 12, 13, 14, 15, 72
Organizations with EU customers using AI for high-risk applications
SOC 2
All five Trust Services Criteria
B2B SaaS companies, enterprise vendors
ISO 42001
All 39 Annex A controls
Organizations seeking formal AI governance certification
FedRAMP
NIST 800-53 (Low, Moderate, High baselines)
US government contractors, federal agencies
TECHNICAL SPECIFICATIONS
Enterprise-grade infrastructure
Performance
- Capture Rate99.7%
- Throughput10K predictions/sec
- Transfer Efficiency (internal testing)93.3%
Deployment
- BYOCAWS, Azure, GCP
- Air-GappedZero external connectivity
- PrivateLinkBypass public internet
- BYOKCustomer-controlled keys
SDKs
- Python (async support)
- TypeScript/JavaScript
- Go
- Java
Authentication
- SSO (SAML 2.0, OIDC)
- RBAC with namespace isolation
- Okta, Azure AD integration
VALIDATED RESULTS
Proven at scale
Lines of production code
Test functions
Audit score (current)
Blockers
Learned patterns
LLM providers
Why 98/100 is better than 100/100
A perfect score invites skepticism. 98/100 is where the self-audit sits today, and the explanation is the point. One of the two open checks is a calibration trend that refuses to pass without enough data. The other is a code-size warning we have not closed. During the failure described in Silent Wiring the same audit read 96, sometimes 97, while four pipelines were dead and every dashboard was green. We don't hide the 2%.
See the full architecture
For investors, strategic partners, and technical deep-dives, we offer platform demonstrations and documentation access.