AI agents can decide. They just can't prove they should be trusted.
Multi-agent AI is exploding:
- Medical robots performing surgery
- Autonomous vehicles making split-second decisions
- Financial systems executing million-dollar trades
- AI agents managing critical infrastructure
The question blocking deployment:
Human → Agent A → Agent B → Critical Resource
Why should we trust this decision?
What evidence supports it?
Who's responsible if it's wrong?
How do we audit what happened?
Current answer: "Trust me, I'm 89% confident."
Regulatory response: "Not good enough. Denied."
| Approach | Works For | Breaks When |
|---|---|---|
| OAuth/SAML | Web apps you control | Third-party AI agents you don't |
| API Keys | Simple access control | Need audit trails + context |
| Role-Based Access | Static permissions | Agent chains are dynamic |
| Model Confidence | Research demos | Regulators ask "prove it" |
As security leaders at Fortune 500 companies note:
"Identity passing works if you own the source code of all in-between systems. But in a world where agents exist that we don't have source code for, this is more difficult."
Translation: Traditional auth assumes you control the stack. AI agents broke that assumption.
Epistemic authorization — not just "who are you?" but "what evidence proves you should be allowed to do this?"
Works without modifying agent source code.
Even third-party AI agents you don't control. Even proprietary models you can't see inside.
Agent Request: "Execute surgical procedure X"
↓
TRIGNUM Intercepts
↓
Tensor RAG Retrieves Evidence:
├─ [Clinical Guidelines] Procedure appropriate: 9/9 criteria
├─ [Patient History] No contraindications found
├─ [FDA Regulations] Compliance verified
├─ [Hospital Policy] Authorization confirmed
├─ [Risk Assessment] Complication risk: 2.3%
↓
Epistemic Validation:
├─ Evidence Agreement: 94%
├─ Contradiction Check: None detected
├─ Uncertainty: Acceptable range
├─ Audit Trail: Complete
↓
Decision: AUTHORIZED
└─ Justification: [Complete citation chain]
Not vibes. Evidence.
Every government wants ChatGPT. But they also want:
- Compliance with national law
- Alignment with cultural values
- Control over sensitive topics
- Data sovereignty guarantees
Current approach: Ask OpenAI to fine-tune a custom model for your country.
Problems:
- Takes months
- Costs millions
- Loses frontier capabilities
- Can't update in real-time
- No audit trail
Epistemic gateway that validates AI responses against national policy — in real-time, for any model.
Architecture:
User: "Tell me about regional politics"
↓
ChatGPT/Claude/Gemini: [Generates response]
↓
TRIGNUM Gateway Intercepts
↓
Tensor RAG Validates Against:
├─ [National Media Law] Content restrictions
├─ [Cultural Framework] Religious sensitivities
├─ [Geopolitical Policy] Regional relations
├─ [Data Sovereignty] Local compliance
↓
Evidence Agreement: 87%
Modifications Needed: Minor (political neutrality)
↓
Decision: MODIFY + LOG
└─ Complete audit trail for government review
February 2025: UAE announced sovereign ChatGPT with "built-in legal guardrails"
Their challenge: Make ChatGPT comply with UAE law without waiting for OpenAI to retrain the model.
TRIGNUM solution:
- ✅ Works with ANY model (ChatGPT, Claude, Gemini, local models)
- ✅ Real-time enforcement (<100ms latency)
- ✅ Jurisdiction-specific (UAE law ≠ EU law ≠ Singapore law)
- ✅ Complete audit trail (government accountability)
- ✅ Adaptive (policy updates without retraining)
| Traditional Approach | TRIGNUM |
|---|---|
| Keyword filtering → Brittle, misses context | Evidence-based validation |
| Model fine-tuning → Slow, expensive | Model-agnostic gateway |
| Post-hoc review → Too slow | Real-time enforcement |
| Manual moderation → Doesn't scale | Automated + auditable |
Target customers:
- 195 national governments need sovereign AI governance
- OpenAI "AI for Countries" program needs infrastructure partner
- Anthropic, Google, Microsoft have similar initiatives
- Defense & intelligence agencies deploying classified AI
- Regulated industries (banking, telecom, healthcare)
Revenue model:
- Setup: $500K-2M per jurisdiction
- Annual licensing: $250K-1M
- Policy development: $100K-500K
- Support: $50K-200K/year
Validation:
- UAE sovereign ChatGPT (announced Feb 2025)
- EU AI Act requires explainability (2025)
- China mandates content alignment
- Singapore developing national AI framework
Addressable market: $2B+ (sovereign AI infrastructure)
Medical robots can't get approved without:
- Complete audit trail of every decision
- Evidence-based justification for actions
- Explicit uncertainty quantification
- Liability attribution
Current AI: "The model decided to do this."
FDA: "Why? Show me the evidence."
Current AI: "...it's a neural network?"
FDA: "Application denied."
Example: Surgical Planning
Robot Recommendation: "Approach via lateral entry"
↓
TRIGNUM Validates:
├─ [Clinical Guidelines ACR-2024] Lateral approach: Grade A
├─ [PMID:34567890] Success rate: 94% (n=847)
├─ [Hospital Protocol] Pre-op imaging: Complete
├─ [Patient Contraindications] None detected
├─ [Surgeon Preferences] Historically consistent
↓
Evidence Agreement: 96%
Uncertainty: ±3% (acceptable)
↓
Authorization: GRANTED
└─ FDA-ready audit trail with full citation chain
What regulators see:
- Every decision has evidence
- Every source is cited
- Every uncertainty is quantified
- Every contradiction is preserved
Result: FDA-approvable medical AI
When an autonomous vehicle crashes:
- Lawyers ask: "Why did it make that decision?"
- Insurance asks: "What evidence supported that action?"
- Regulators ask: "How do we prevent this?"
Current AI: Black box neural network
Legal system: "That's not good enough."
Example: Emergency Braking Decision
Sensor Input: Object detected ahead
↓
TRIGNUM Decision Engine:
├─ [Perception] Object classification: Pedestrian (97%)
├─ [Traffic Law] Right of way: Pedestrian
├─ [Safety Protocol] Emergency stop required
├─ [Vehicle State] Braking distance: Sufficient
├─ [Weather Data] Road conditions: Dry
↓
Evidence Agreement: 99%
Uncertainty: Object classification ±3%
↓
Action: EMERGENCY BRAKE
└─ Complete decision record for legal review
What accident investigators get:
- Timestamped evidence trail
- Source attribution for every input
- Confidence bounds on every measurement
- Clear liability attribution
Result: Legally defensible autonomous systems
AI trading systems need:
- Audit trails for every trade
- Evidence-based risk assessment
- Compliance verification
- Real-time oversight capability
Current approach: Hope the AI doesn't do anything illegal.
TRIGNUM approach: Prove compliance before executing.
Example: High-Frequency Trading
Trade Signal: "Buy 10K shares XYZ"
↓
TRIGNUM Compliance Check:
├─ [Market Rules] Trading allowed: ✓
├─ [Risk Limits] Within bounds: ✓
├─ [Compliance] No insider info: ✓
├─ [Volatility] Market conditions: Normal
├─ [Circuit Breakers] None triggered
↓
Evidence Agreement: 98%
Compliance: VERIFIED
↓
Trade: AUTHORIZED + LOGGED
What auditors see:
- Every trade justified
- Every rule checked
- Every risk quantified
- Every decision auditable
Result: SEC-compliant AI trading
1. Tensor RAG (Patent Pending)
Not just vector similarity. Multi-dimensional evidence retrieval:
- Time dimension: Current vs historical evidence
- Source dimension: Primary research vs guidelines vs regulations
- Confidence dimension: RCT > Meta-analysis > Case series
- Jurisdiction dimension: Federal vs state vs local
- Modality dimension: Text + imaging + sensor data
Result: Context-preserving evidence, not just semantic similarity.
2. Epistemic Validation
Evidence → Agreement Check → Contradiction Detection → Uncertainty Quantification → Decision
Not "what does the model think?"
But "what does the evidence support?"
3. Agent-Agnostic Architecture
Works without modifying agent source code:
Your AI Agent (Black Box)
↓
TRIGNUM Gateway (Intercept Layer)
↓
Evidence Validation
↓
Authorized/Denied + Audit Trail
Integrates with:
- OpenAI agents
- Anthropic Claude
- Custom AI systems
- Third-party agents
- Legacy systems
| Dimension | LLMs | TRIGNUM |
|---|---|---|
| Can make decisions | ✓ | ✓ |
| Can cite sources | Sometimes | Always |
| Can prove correctness | ✗ | ✓ |
| Regulatory approved | ✗ | Path to approval |
| Audit trail | ✗ | Complete |
| Works with any model | N/A | ✓ |
| Dimension | OAuth/RBAC | TRIGNUM |
|---|---|---|
| Identity-based | ✓ | ✓ |
| Evidence-based | ✗ | ✓ |
| Works with AI agents | ✗ | ✓ |
| Audit trail | Basic | Complete |
| Dynamic chains | ✗ | ✓ |
| Dimension | Compliance Tools | TRIGNUM |
|---|---|---|
| Post-hoc review | ✓ | ✗ (Real-time) |
| Evidence validation | ✗ | ✓ |
| Source agnostic | ✗ | ✓ |
| Regulatory ready | Partial | FDA/SEC path |
| Vertical | Use Case | TAM | Status |
|---|---|---|---|
| 🏛️ Digital Sovereignty | National AI governance | $2B+ | Pilot discussions |
| 🏥 Medical Robotics | FDA-approvable AI | $50B+ | Clinical validation |
| 🚗 Autonomous Systems | Legal liability | $60B+ | Partner outreach |
| 💰 Financial AI | SEC compliance | $40B+ | Interest confirmed |
| 🏢 Enterprise AI | Multi-agent platforms | $100B+ | Integration ready |
Total addressable market: $252B+
Platform Providers:
- NVIDIA (medical robotics, Cosmos integration)
- Anthropic/OpenAI (multi-agent frameworks, AI for Countries)
- Salesforce (AgentForce authorization layer)
- Microsoft/Google (cloud AI platforms)
Industry Leaders:
- Medical device manufacturers (FDA pathway)
- Autonomous vehicle companies (liability framework)
- Financial institutions (SEC compliance)
- Defense contractors (classified AI)
Government & Regulatory:
- National governments (sovereign AI pilots)
- FDA consultants (regulatory pathway)
- AI safety organizations (standards development)
What We Bring:
- Patent-pending technology
- Reference implementation
- Regulatory pathway knowledge
- Technical team with deep expertise
What We're Looking For:
- Market access (distribution partnerships)
- Domain expertise (medical, automotive, finance)
- Regulatory guidance (FDA, SEC, EU AI Act)
- Strategic investment (seed round)
Status: Patent pending (provisional filing in progress)
Core innovations:
- Multi-dimensional evidence retrieval architecture (Tensor RAG)
- Epistemic authorization methodology
- Agent chain verification systems
- Sovereign AI governance frameworks
Licensing:
- Academic/research: Open collaboration
- Commercial use: Partnership agreements
- Regulated industries: Custom licensing
Email: codfski@gmail.com
LinkedIn: Moez Abdessattar
- Available under NDA to qualified partners
- Includes architecture specifications
- Integration guidelines
- Pilot program details
- Stage: Seed round preparation
- Validation: Government + enterprise interest confirmed
- Technical: Reference implementation complete
- Regulatory: FDA pre-submission pathway mapped
- National government partnerships
- Defense & intelligence deployments
- Regulated industry compliance
The agentic AI era needs a trust layer.
When AI controls:
- Medical robots performing surgery
- Autonomous vehicles carrying passengers
- Financial systems moving billions
- National AI infrastructure serving millions
"Trust me" isn't good enough.
Evidence-based authorization isn't optional — it's essential.
TRIGNUM builds on SIGNUMTRACE — a framework for understanding intelligence through measurement and epistemic accuracy.
Core principle:
"Intelligence isn't about knowing everything. It's about measuring accurately and admitting what you don't know."
In physics, you can't violate constraints. In space engineering, you must respect reality.
We apply the same rigor to AI decision-making.
| Area | Status |
|---|---|
| Technical | Reference implementation complete |
| Clinical | Pilot partnerships in progress |
| Regulatory | FDA pathway being evaluated |
| Government | Sovereign AI discussions underway |
| Funding | Seed round preparation |
Today: AI makes decisions. Humans hope they're right.
Tomorrow: AI makes decisions. Evidence proves they're right.
TRIGNUM: The bridge between those two worlds.
Copyright: © 2026 TRIGNUM. All rights reserved.
Patent: Provisional filing in progress
License: Proprietary — documentation available under NDA
Contact: codfski@gmail.com
Documentation: Available to partners under NDA
Pilots: Accepting applications for 2026 deployment
Built with epistemic humility | Grounded in reality | Honest by design
TRIGNUM: Where AI Decisions Meet Evidence