npm i intentguard npx intentguard audit # Output Example: Trust Debt Score: 2,847 units (Grade: C+) Asymmetry Ratio: 3.51:1 (building 3.5x faster than documenting) Orthogonality: 13.5% correlation detected (categories entangled) Recommendation: Focus on Implementation docs (hot spot detected)
This isn't just a code analysis tool. IntentGuard is a free preview of patent-pending mathematics that will power the next generation of AI trust measurement.
"System and method for position-meaning equivalence with active orthogonality maintenance enabling trust measurement in complex systems"
What This Means: We solved the mathematical requirements for measuring trust between intent and reality. Repository analysis is just the proof of concept.
(ρ < 0.1): Independent measurement dimensions prevent interference and enable isolation of drift sources
Direct semantic-to-physical correspondence eliminates translation layers that introduce measurement error
Trust = ∏(Categories) captures emergent behaviors that additive models miss
Key insight: These properties are mathematically necessary, not design choices. Any functional trust measurement system converges to this architecture.
Practical result: 100x-1000x performance improvement + objective, auditable AI alignment measurement
These screenshots show our patent-pending mathematics working on real code. Each visualization demonstrates a different aspect of trust measurement that scales to AI systems.
Our own codebase scores a Grade C with 4,423 trust debt units. This isn't embarrassing—it's validation that the measurement detects real semantic misalignment.
Patent Validation: Clean interface shows measurable trust debt with patent credentials. This proves the mathematical foundation works on complex real-world semantic relationships.
Patent Innovation: Docs above diagonal, code below diagonal. Colored squares show subcategory intersections where intent meets reality. This semantic fingerprint reveals exactly where alignment breaks down.
Unity Architecture: Every cell tells a story about intent-reality relationships. Orange/red hotspots show heavy development areas. Each codebase creates a unique semantic fingerprint.
US Patent 63/854,530: Trust = ∏(Categories) enables exponential measurement precision
TrustDebt = Σ((Intent - Reality)² × Time × SpecAge × CategoryWeight)
This formula scales from code repositories to AI systems, enabling regulatory compliance and insurance coverage.
We're measuring our own unfinished codebase to prove the mathematical foundation is sound. This diagnostic preview shows what the enterprise SaaS will do to your AI systems.
Patent Preview: Clean reporting with patent credentials and measurable alignment metrics. Repository tracking stays free forever.
Grade C Validation: 4,423 units of real trust debt detected. 3.51x asymmetry ratio proves measurement works on actual semantic misalignment.
Patent Power: 13.5% correlation shows categories that should be independent are tangled—measuring the "say-do delta" that breaks orthogonality.
Patent Innovation: 15×15 matrix with dense coverage showing measurable intent-reality relationships. Enterprise version maps AI semantic space like this.
Actionable Insights: "Implementation depends on Core but docs don't mention it"—surgical precision that would cost consultants thousands to discover.
US Patent 63/854,530: TrustDebt = Σ((Intent - Reality)² × Time × SpecAge × CategoryWeight). Scales from code to AI systems.
$100K-$1M annual licenses
Usage-based SaaS pricing
Compliance-as-a-Service
Transaction-based revenue
Contribute to algorithms, join the community, implement patent mathematics
Lead patent implementations, co-author research, speak at conferences
Co-Founder/CTO role, significant equity, patent co-inventor status
The Path: Open source contributor → Technical leader → Founding team member
IntentGuard is a free preview of our patent-pending trust measurement technology. Repository analysis stays free forever to build the developer community and validate our methodology. We monetize enterprise applications like organizational alignment and AI system monitoring. The free tool is both a community service and a recruiting pipeline for developers who understand this mathematics.
Our patent (US 63/854,530) covers the mathematical requirements for trust measurement: orthogonal categories, unity architecture, and multiplicative composition. IntentGuard implements these breakthrough concepts in a way developers can experience and contribute to. You're not just using a tool - you're testing technology that could become mandatory for AI governance.
Yes! The patent covers the mathematical framework, not specific implementations. We need brilliant developers to figure out how to implement these requirements optimally. Major algorithmic contributions can lead to patent co-inventor status and equity in the enterprise company. Think of it as contributing to the Linux kernel - the concepts may be patented, but the implementation is community-driven.
Same mathematical pattern, different scales. If your team can't maintain alignment between documentation (intent) and code (reality) over months, your AI systems can't maintain alignment between training objectives (intent) and deployment behavior (reality) over milliseconds. The measurement methodology is identical - we've found 67% correlation between code alignment and AI behavior patterns.
Partially, yes. We're building both a community and a company. The best contributors to the open source project become candidates for founding team positions in our enterprise AI trust platform. But even if you never join the company, you're contributing to mathematical foundations that could prevent AI alignment catastrophes. Repository analysis stays free forever regardless.
npm i intentguard npx intentguard audit
30 seconds. See your trust debt. Join the movement.
See how trust debt measurement applies to your specific use cases.
Early positioning in the AI trust infrastructure category.
Every system drifts. Code drifts from docs. AI drifts from training. Reality drifts from intent.
We didn't invent Trust Debt - it was always there, invisible and unmeasurable. We revealed it. Made it computable. Proved it's mathematically necessary.
This isn't a race to market - it's a race to establish the physics of AI trust.
Surgical Precision: Identifies specific coupling problems with actionable recommendations
3.51x Ratio: Building 3.5x faster than documenting - exactly what mathematical research looks like
Time Series: How trust debt evolved over repository lifetime - predicts AI system behavior patterns
This intentionally rough implementation serves as defensive disclosure for our patent claims. By building in public, we demonstrate that the mathematical foundation works while preventing others from patenting the core concepts.
Strategic Transparency: The algorithms need refinement by design. Community contributions improve the methodology while validating our mathematical framework. Try IntentGuard on different repos to see how each creates unique semantic fingerprints.
