VectorCertain Unveils Its 55-Patent AI Safety, Compliance & Optimization Ecosystem

VectorCertain LLC today disclosed its comprehensive 55-patent intellectual property portfolio — the first AI safety architecture built on a governance-first, permission-to-act paradigm that spans autonomous vehicles, cybersecurity, healthcare, financial services, blockchain/DeFi, energy infrastructure, manufacturing, satellite systems, content moderation, and government AI certification.

Of the 55 patents in the ecosystem, 21 have been filed (7 in December 2025, 12 in January 2026) with the remaining 18 in active development and scheduled for filing through 2026. The portfolio encompasses over 500 claims, with every filed application scoring 10.0/10 on independent quality assurance review.

Artificial intelligence systems do not self-authorize. All AI decisions are subject to independent, runtime governance determining whether they may be trusted, relied upon, or acted upon. This is the core paradigm that unifies our entire 55-patent ecosystem.” — Joseph P. Conroy, Founder & CEO, VectorCertain LLC

Unlike bolt-on safety layers or post-hoc auditing frameworks, VectorCertain’s patents are architected from the ground up around a single principle: AI must earn permission to act, every time, through mathematically verifiable independent governance. This paradigm replaces model-centric safety, optimization-centric AI, and retrospective validation with governance-first, permission-to-act safety.

Portfolio Composition

• Safety & Governance (SG) Patents: 15 patents covering core governance hubs, blockchain sub-hub, and domain-specific safety spokes — approximately 350 claims — 14 filed (January 2026)

• Application Spoke Patents: 22 patents covering 12 industry verticals — approximately 380 claims — 5 filed (December 2025)

• Total Ecosystem: 55 patents, approximately 730 claims, 21 filed to date, all filed patents scoring 10.0/10 on independent QA review

• Pipeline: 30 additional patents in active development spanning autonomous vehicles, satellite systems, content moderation, government AI certification, energy, manufacturing, supply chain, legal/regulatory, and additional financial services applications. Filings scheduled through 2026.

• Not included: 4 additional patents emerging from 22 sprints of SecureAgent platform development, currently in documentation.

The Hub & Spoke Architecture: Governance-First by Design

VectorCertain’s 55-patent ecosystem is organized in a three-layer hub-and-spoke architecture where authority flows from governance hubs down through application spokes. This structure ensures that no application ever redefines safety — it only applies governance defined at the hub level.

Layer 1: Core Safety Governance Hubs (Foundational Authority)

These patents define what is allowed. They establish the mathematical and epistemic foundations for AI trust, numerical safety, and execution permission. They are domain-agnostic and serve as the authoritative root for the entire portfolio.

• HCF2-SG — Epistemic Trust Governance (Primary Hub): Determines whether an AI decision is trustworthy at all. Four-layer independence verification including architectural, statistical, error-focused, and adaptive methods. False consensus detection and correlated failure identification. SPRT-based sequential consensus.

• TEQ-SG — Numerical Admissibility Governance: Determines whether numerical approximation preserves safety. Monitors reduced precision effects, rare-event sensitivity, and numerical correlation collapse. Consensus-preserving compression achieving 3.92–4.12X compression while maintaining ASIL-D compliance.

• MRM-CFS-SG — Execution Governance (Micro-Recursive Model Cascading Fusion System): Determines whether a trusted, admissible decision may be acted upon now. 256 models in <50KB, >99% tail-event accuracy, individual models as small as 29–71 bytes. Runtime constraint enforcement with fallback behavior.

• MRM-CFS (Standalone) — Micro-Recursive Model Architecture: Independent MRM deployment for tail-event detection. 97.82% R² accuracy at just 71 bytes — 3–4 orders of magnitude smaller than TinyML minimums. Enables deployment on 8-bit and 16-bit legacy processors.

• GD-CSR — Graceful Degradation Through Combinatorial Sensor Redundancy: Mathematically proven no-blind-spot guarantee under sensor failure. C(N,2) combinatorial clustering with 5X overlap coverage. ASIL-D, PLd, and DAL-A compliance for autonomous, industrial, and aerospace applications.

• HES1-SG — Candidate Diversity Generation: Supplies diverse candidate decisions via cross-architecture consensus. Tier A transformers (7B–175B+ parameters) combined with Tier E recurrent models (5–30M parameters). 67–75% error correlation reduction. Explicitly subordinate to governance hubs — non-authoritative.

Layer 2: Domain Governance Sub-Hub (Blockchain Safety Governance)

Blockchain environments break the assumptions of bounded execution, identifiable operators, and enforceable controls. The BC-SG (Blockchain Safety Governance) sub-hub extends and cryptographically enforces the core hubs under adversarial, decentralized conditions. BC-SG is not a spoke — it is a proof layer for governance itself.

• DeFi-SG — Financial Risk Governance: Governs DeFi liquidation, leverage, and exposure decisions. Copula-based cross-protocol tail dependence analysis prevents cascading systemic failure across interconnected DeFi protocols.

• MEV-SG — Transaction Execution Governance: Enforces fairness and safety in transaction ordering. Treats Maximal Extractable Value as a governance problem, not an inevitability. Execution permission under safety constraints.

• ZKML-SG — Cryptographic AI Verification: Zero-knowledge proofs verify AI decisions without revealing models or data. Enables trust governance in adversarial environments where model transparency is impossible.

• DAICON-SG — Distributed AI Consensus Governance: Governs distributed AI consensus across decentralized networks. Detects epistemic failure in decentralized agreement. Separates consensus from correctness — agreement alone does not establish safety.

Layer 3: Application Spokes (Where Governance Is Applied)

Each application spoke applies governance defined by the hubs. Spokes never redefine safety and never claim authority. They are replaceable, expandable, and non-fragile — designed so that the portfolio can scale to new industries without structural modification.

The 22 application spokes span 12 distinct industry verticals. The following 7 have been filed as provisional patent applications:

• HCF2-PROV-ENHANCED — Hierarchical Cascading Framework (Enhanced): Four-layer independence verification engine. Architectural, Statistical, Error-Focused, and Adaptive verification methods with SPRT-based sequential consensus. Filed December 2025.

• HES1-PROV — Hybrid Ensemble System: Cross-architecture consensus implementation combining Tier A transformers (7B–175B+) with Tier E recurrent models (5–30M parameters). Filed December 2025.

• TEQ-PROV — Temperature-Scaled Ensemble Quantization: Consensus-preserving compression for edge deployment. 3.92–4.12X compression with <0.2% degradation while maintaining ASIL-D compliance. Filed December 2025.

• ICCS-PROV — Insurance Claims Compliance System: AI-powered insurance claims processing with ensemble-verified fraud detection, compliance automation, and NAIC Model Bulletin regulatory audit trails. Filed December 2025.

• CMTD-PROV — Cybersecurity Monitoring & Threat Detection: Cross-architecture consensus for threat detection. MITRE ATT&CK integration across 14 tactics and 200+ techniques. Temporal novelty detection and adversarial evasion (ATIT) countermeasures. Filed December 2025.

• HC-PROV — Healthcare Claims Processing: Emerging Billing Pattern Detection achieving <72-hour fraud detection vs. the 18+ month industry average. FDA PCCP alignment for clinical decision support. Filed December 2025.

• ETS-PROV — Electronic Trading Systems: Tail dependence analysis for trading risk with flash crash prevention. Demonstrated 77% drawdown reduction in back-testing against historical market events. Filed December 2025.

The remaining 15 application spoke patents are in active development, spanning autonomous vehicles, satellite/aerospace, content moderation, government AI certification, energy grid optimization, manufacturing quality control, supply chain resilience, legal/regulatory monitoring, financial fraud detection, trade reconciliation, and additional blockchain/DeFi applications. These patents are scheduled for filing through 2026.

Three Operating Domains: Safety, Applications, and Real-Time Compliance

Domain A: Safety & Compliance (The Governance Layer)

VectorCertain’s Safety & Governance patents define the authority layer — the mathematical and epistemic foundations that determine when AI may be trusted. This domain encompasses the 6 core hub patents, 4 blockchain sub-hub patents, and 5 domain-specific governance spokes, totaling 15 safety & governance patents with approximately 350 claims.

Core capability — Permission-to-Act Verification: Every AI decision passes through four sequential gates before any safety-critical action is authorized:

• Gate 1 — Present Data: Sensor data and model inputs are presented to the governance layer for evaluation.

• Gate 2 — Assess Data Validity: Four complementary verification methods independently evaluate the data, achieving 94–98% correlation detection for identifying correlated failures.

• Gate 3 — Permission to Quantify: TEQ ensures that numerical approximation preserves safety properties. Only data that survives quantization without degrading safety margins proceeds.

• Gate 4 — Permission to Execute: MRM-CFS performs cross-architecture consensus. AI must pass all four gates before any action is authorized. If consensus fails, the system abstains, escalates, or falls back to a known-safe state.

Regulatory Alignment & Real-Time Compliance Monitoring

VectorCertain’s architecture natively addresses 47+ regulatory frameworks. Critically, compliance is not a periodic audit function — it is a continuous, real-time property of the system’s operation. Every inference generates auditable compliance evidence automatically, with comprehensive recording of all mission-critical events.

Regulatory frameworks addressed:

• Autonomous Vehicles: ISO 26262 (ASIL-D), ISO PAS 8800, NHTSA AV Guidelines, SAE J3016. Real-time monitoring of functional safety metrics with continuous audit trail of every sensor fusion decision, model consensus outcome, and fallback activation.

• Healthcare & Medical Devices: FDA 21 CFR Part 11 (electronic records/signatures), FDA PCCP (Predetermined Change Control Plan), HIPAA, IEC 62304 Class C, ISO 14971. Every clinical decision support recommendation includes timestamped model identity, confidence scores, consensus results, and escalation rationale — constituting a complete electronic signature under 21 CFR Part 11.

• Financial Services: OCC SR 11-7 (Model Risk Management), SEC Rule 17a-4 (records retention), Basel III/IV, Dodd-Frank. Cross-architecture consensus between structurally different model families satisfies the OCC’s requirement for “critical analysis by objective, informed parties.” Disagreement metrics, individual model predictions, and escalation outcomes are logged and retained automatically.

• Insurance: NAIC Model Bulletin on AI, state-specific insurance regulations. Comprehensive audit trail of every claims decision including model consensus, fraud detection triggers, and compliance checkpoints.

• Cybersecurity: NIST Cybersecurity Framework, MITRE ATT&CK, SOC 2 Type II. Continuous monitoring of threat detection decisions with real-time recording of analyst fatigue indicators, false positive rates, and cross-model consensus scores for every alert escalation.

• Energy & Critical Infrastructure: NERC CIP (Critical Infrastructure Protection), IEEE 2030, FERC standards. Real-time audit trail of grid optimization decisions, load balancing actions, and cascade failure prevention interventions with millisecond-level timestamping.

• Blockchain & DeFi: EU MiCA (Markets in Crypto-Assets), SEC digital asset guidance, FinCEN AML requirements. Cryptographic verification through ZKML enables compliance proof without exposing proprietary models or user data.

• Content Moderation: EU AI Act (High-Risk AI Systems), EU Digital Services Act (DSA), platform-specific content policies. Comprehensive audit trail of every content decision for regulatory reporting.

• Government & Defense: NIST AI Risk Management Framework (AI RMF), CMMC (Cybersecurity Maturity Model Certification), FedRAMP, DO-178C (DAL-A). Real-time compliance monitoring with immutable audit records suitable for federal inspection and accreditation.

• Manufacturing: ISO 13849 (PLd), IEC 61508 (SIL 3), FDA 21 CFR Part 820 (Quality System Regulation). Continuous recording of quality control decisions, defect detection consensus, and production line safety interventions.

• Aerospace & Satellite: DO-178C (DAL-A), NASA-STD-8739.8, ITU Radio Regulations. Mission-critical event recording for every collision avoidance decision, orbital adjustment, and radiation-induced anomaly response.

Real-Time Compliance Infrastructure

Across all regulated domains, VectorCertain provides the following compliance infrastructure as inherent properties of runtime operation:

• Cascade Audit Trails: Each transition between HCF2 cascade tiers automatically generates timestamped compliance records including triggering confidence thresholds, routing rationale, cryptographic hashes of input data, and model identity as electronic signatures. These records are immutable and tamper-evident.

• Effective Challenge Documentation: Cross-architecture consensus between Tier A transformers and Tier E recurrent models satisfies regulatory requirements for independent challenge and objective analysis. Disagreement metrics, individual model predictions, consensus confidence intervals, and escalation outcomes are logged automatically for every decision.

• Comprehensive Mission-Critical Event Recording: Every safety-critical inference produces a complete event record: input data hashes, pre-processing transformations, individual model outputs, consensus scores, gate pass/fail results, and final disposition (execute, inhibit, escalate, or abstain). Records are structured for regulatory examination across all applicable frameworks.

• Edge-to-Cloud Audit Synchronization: TEQ’s consensus-preserving quantization maintains compliance properties when deployed on edge devices. Lightweight audit buffers and cryptographic hash chains ensure integrity despite network interruptions, with full synchronization upon reconnection.

• 24-Hour Regulatory Detection: Automated regulatory monitoring detects new requirements, amendments, and enforcement actions within 24 hours vs. 2–4 weeks for manual review, providing 6–12 month compliance head starts for organizations subject to evolving regulatory frameworks.

• Cross-Jurisdictional Compliance Mapping: Governance architecture maps compliance obligations across 47+ frameworks simultaneously, enabling organizations to demonstrate compliance to multiple regulators from a single audit trail rather than maintaining separate compliance programs for each authority.

Domain B: Applications (The Spoke Layer)

The 22 application patents implement governance across 12 industry verticals. Each spoke is designed as a modular, independently deployable system that inherits authority from the hub layer while addressing industry-specific operational and regulatory requirements.

• Autonomous Vehicles: L4 certification pathway, ASIL-D compliance, tail-event detection, MRM-CFS + GD-CSR sensor redundancy integration

• Cybersecurity: MITRE ATT&CK cross-architecture consensus, analyst fatigue detection, SOC governance, adversarial evasion countermeasures

• Healthcare: 72-hour fraud detection (vs. 18+ months), FDA PCCP alignment, Class III medical device approval pathway

• Financial Services: Flash crash prevention, 77% drawdown reduction, T+1 trade reconciliation with Byzantine fault tolerance

• Insurance: Ensemble-verified claims compliance, NAIC Model Bulletin alignment, automated fraud detection with regulatory audit trails

• Blockchain/DeFi: Cryptographic governance, transaction ordering fairness, distributed consensus safety, zero-knowledge compliance verification

• Energy: Grid stability monitoring, cascade failure prevention 15–30 minutes before initiation, NERC CIP compliance

• Manufacturing: Multi-modal fusion quality control, adaptive defect prediction, production line safety with ICS integration

• Satellite/Aerospace: Collision avoidance, radiation-hardened AI governance, DAL-A compliance for mission-critical orbital operations

• Content Moderation: EU AI Act and DSA compliance, cross-architecture tail-event content detection for CSAM, terrorism, and hate speech

• Government AI: Federal AI certification framework, NIST AI RMF alignment, CMMC and FedRAMP compliance

• Supply Chain: Multi-modal disruption prediction, autonomous risk mitigation, supplier reliability consensus

Domain C: Real-Time Compliance Capability

A critical differentiator of VectorCertain’s architecture is that compliance is not a separate audit function — it is an inherent property of runtime operation. Every inference, every consensus decision, and every permission-to-act gate generates auditable compliance evidence automatically. This real-time compliance capability eliminates the gap between “operating the AI system” and “proving it was operated safely” — they become the same activity.

The complete real-time compliance infrastructure is detailed in the Regulatory Alignment section above, including cascade audit trails, effective challenge documentation, comprehensive mission-critical event recording, edge-to-cloud audit synchronization, 24-hour regulatory detection, and cross-jurisdictional compliance mapping.

Filed Patent Registry: 21 Patents Filed to Date

The following 21 provisional patent applications have been filed with the United States Patent and Trademark Office. Each application scored 10.0/10 on independent quality assurance review.

Safety & Governance Patents (14 Filed — January 2026)

• HCF2-SG: Epistemic Trust Governance — Primary hub patent. Independence verification, false consensus detection, correlated failure identification.

• TEQ-SG: Numerical Admissibility Governance — Quantization safety, rare-event sensitivity monitoring, numerical correlation collapse detection.

• MRM-CFS-SG: Execution Governance — 256 micro-recursive models in <50KB. Runtime permission-to-act enforcement.

• MRM-CFS (Standalone): Micro-Recursive Model Architecture — 71-byte neural networks, 97.82% R² accuracy.

• GD-CSR: Graceful Degradation Through Combinatorial Sensor Redundancy — No-blind-spot guarantee, 5X overlap coverage.

• HES1-SG: Candidate Diversity Generation — Cross-architecture consensus, 67–75% error correlation reduction.

• Insurance-CCS-SG: Insurance Claims Compliance & Safety Governance — NAIC Model Bulletin alignment.

• Cybersecurity-SG: AI Cybersecurity Governance — Three-layer governance, MITRE ATT&CK integration, 50 claims.

• Medical-SG: Healthcare Safety Governance — FDA 21 CFR Part 11, HIPAA, clinical decision support governance.

• AutoSafety-SG: Autonomous Vehicle Safety Compliance — ASIL-D certification pathway, ISO 26262, NHTSA alignment.

• DeFi-SG: Decentralized Finance Risk Governance — Liquidation and exposure governance, cascading failure prevention.

• MEV-SG: Transaction Execution Governance — Transaction ordering fairness, extraction prevention.

• ZKML-SG: Cryptographic AI Verification — Zero-knowledge proof verification of AI model outputs.

• DAICON-SG: Distributed AI Consensus Governance — Epistemic failure detection in decentralized agreement.

Application Spoke Patents (5 Filed — December 2025)

• HES1-PROV (VC-2025-HES1-PROV): Hybrid Ensemble System — Cross-architecture consensus implementation.

• TEQ-PROV (VC-2025-TEQ-001-PROV): Temperature-Scaled Ensemble Quantization — Consensus-preserving compression for edge deployment.

• ICCS-PROV (VC-2025-ICCS-001-PROV): Insurance Claims Compliance System — Ensemble-verified fraud detection and compliance automation.

• CMTD-PROV (VC-2025-CMTD-001-PROV): Cybersecurity Monitoring & Threat Detection — MITRE ATT&CK consensus and adversarial evasion countermeasures.

• HC-PROV (VC-2025-HC-001-PROV): Healthcare Claims Processing — 72-hour emerging billing pattern detection, FDA PCCP alignment.

Additional Patents in Development

18 additional patents are in active development and scheduled for filing through 2026. These patents extend the ecosystem into autonomous vehicles, satellite/aerospace, content moderation, government AI certification, energy grid optimization, manufacturing quality control, supply chain resilience, legal/regulatory monitoring, financial fraud detection, trade reconciliation, and additional blockchain/DeFi applications. Specific patent disclosures will be made upon filing.

$1.777 Trillion in Validated Prevented Losses: Historical Back-Casting

VectorCertain validated its technology against more than 50 catastrophic failures spanning 2000–2024 across 11 industries. By applying the patent-pending permission-to-act architecture to historical failure data, VectorCertain demonstrated that $1.777 trillion in losses were preventable.

This back-casting methodology provides concrete, verifiable evidence that governance-first AI safety is not theoretical — it addresses real-world failures that have already occurred and quantifies the economic impact of prevention.

Autonomous Vehicles — $476 Billion in Prevented Losses

Tesla highway fatalities — cross-modal radar verification would have provided 8.3 seconds of advance driver warning and reduced collision energy by 78%. GD-CSR’s no-blind-spot guarantee prevents sensor degradation failures in rain, fog, and snow. MRM-CFS tail-event detection identifies the rare distribution-edge scenarios where perception systems fail catastrophically.

Financial Fraud — $557 Billion in Prevented Losses

Compound medication fraud ($500M exposure) — HC-PROV’s Emerging Billing Pattern Detection would have identified the scheme within 72 hours vs. the actual 36-month discovery timeline, limiting exposure to less than $2M. Cross-architecture consensus flags anomalous billing patterns that single-model fraud detection systems consistently miss.

Manufacturing Quality Control — $300 Billion in Prevented Losses

Takata airbag recall ($10B cost) — geographic clustering analysis would have detected Florida humidity-related propellant failures 6–7 years before the recall, preventing 43 million defective units from reaching consumers. Multi-modal fusion inspection validated against actual defect data from the failure event.

Energy Grid Systems — $93 Billion in Prevented Losses

Northeast Blackout (2003, 55 million affected) — tail dependence analysis would have detected correlated equipment failures 15–30 minutes before cascade initiation, enabling protective load shedding. Real-time grid governance prevents the cascading failures that transform localized equipment faults into regional blackouts.

Regulatory Compliance — $54 Billion in Prevented Losses

24-hour regulation detection vs. 2–4 weeks for manual review provides 6–12 month compliance head starts, preventing $44–54 billion in shareholder losses through early compliance detection and proactive response to evolving regulatory requirements.

Financial Trading — $25 Billion in Prevented Losses

Flash Crash (2010) and COVID market crash (2020) — tail dependence detection across correlated instruments would have triggered protective position reduction, achieving 77% drawdown reduction. MRM-CFS identifies the rare-event conditions that precede systemic market dislocations.

Cybersecurity — $20 Billion in Prevented Losses

SolarWinds supply chain attack — cross-architecture detection would have identified anomalous network behavior approximately 9 months earlier, reducing the 14-month dwell time. MITRE ATT&CK integration across 14 tactics and 200+ techniques provides comprehensive threat coverage.

Total Validated Prevented Losses: $1.777 Trillion

Across 50+ catastrophic failures, 11 industries, 2000–2024. All estimates are conservative, based on publicly available failure data and established actuarial methodologies.

Back-Casting Methodology

Each case study applies the specific patent technology to historical sensor data, transaction records, or system logs from the actual failure event. The analysis determines: (a) at what point the permission-to-act architecture would have detected the anomaly, (b) what governance action would have been triggered — inhibit, escalate, or abstain, and (c) the resulting reduction in economic loss based on the earlier intervention window.

Why This Portfolio Is Unique

No Existing Patents Occupy This White Space

Analysis of 1,600+ AI governance patents from IBM, 5,000+ AI patents from automotive OEMs, 1,100+ AI patent families from Siemens Healthineers, and comprehensive searches across Google/DeepMind, Microsoft, and NVIDIA portfolios reveals consistent gaps where VectorCertain’s governance-first ensemble claims are novel.

• vs. IBM (7,000+ AI patents): IBM focuses on single-model governance through watsonx.governance. No ensemble-specific compliance claims; no multi-model consensus as regulatory “effective challenge.”

• vs. Google/DeepMind: Focus on alignment through Frontier Safety Framework. No compliance-focused ensemble validation; no audit trail for ensemble decisions.

• vs. Microsoft: US12299140B2 (Citibank/Microsoft) covers “multi-model superstructure” but uses same-architecture models. Lacks cross-architecture independence and regulatory mapping.

• vs. NVIDIA: Focus on hardware optimization through TensorRT. No software-level ensemble compliance governance; no audit synchronization for edge models.

• vs. Automotive OEMs: Focus on sensor fusion and perception with ISO 26262 compliance through hardware safety. No ensemble model validation for software-level safety certification.

Structural Advantages of Hub & Spoke Architecture

Patent defensibility: The hub-and-spoke structure prevents terminal disclaimer sprawl, obviousness collapse, and examiner confusion. Core hubs anchor priority while spokes are independently expandable.

Licensing flexibility: The modular architecture enables industry-specific licensing bundles. An automotive licensee accesses AV-SG + AutoSafety-SG + MRM-CFS + GD-CSR without requiring blockchain patents. A DeFi platform licenses BC-SG sub-hub patents without autonomous vehicle IP.

Future-proofing: New application spokes can be added to the portfolio without modifying core hub patents. As new industries adopt AI in safety-critical applications, VectorCertain can extend the ecosystem with additional spokes while maintaining the same governance authority.

Key Technical Specifications

MRM-CFS (Micro-Recursive Model Cascading Fusion System)

• Individual model size: 29–71 bytes (INT8), up to 209 bytes max

• Parameters per model: 25–209 (average 89)

• Total MRMs (8-camera system): 828 models

• Total memory footprint: <50 KB (full autonomous driving ensemble)

• Inference latency: <1 ms (entire 828-model ensemble)

• Energy per inference: <10 picojoules per MRM

• Tail-event accuracy: >99% (vs. 60–70% for traditional neural networks at distribution tails)

• Hardware compatibility: 8-bit and 16-bit legacy processors (no hardware upgrade required)

Ensemble Independence & Consensus

• Pairwise model correlation: <0.5 (vs. >0.81 for LLM-based ensembles)

• Error correlation reduction: 0.80–0.85 → 0.10–0.20 (67–75% reduction via cross-architecture consensus)

• Quantization degradation: <0.2% (FP32 → INT8)

• Compression ratio: 3.92–4.12X while maintaining ASIL-D compliance

GD-CSR (Graceful Degradation Through Combinatorial Sensor Redundancy)

• Overlap per sensor: 5X (for N=6 peripheral cameras)

• Blind-spot guarantee: Mathematically proven — No-Blind-Spot Lemma under single-sensor failure

Safety Certifications Targeted

• Automotive: ASIL-D (ISO 26262 highest integrity level)

• Industrial: ISO 13849 PLd, IEC 61508 SIL 3

• Medical: IEC 62304 Class C

• Aerospace: DO-178C DAL-A (highest design assurance level)

Market Opportunity

• Addressable market (Safety-Critical AI): $157–240 billion by 2030

About VectorCertain

VectorCertain LLC is a Delaware corporation headquartered in Maine, specializing in AI safety and governance technology. Founded by Joseph P. Conroy, a 30-year AI systems veteran who achieved an eight-figure exit with Envapower, an AI electricity price forecast for NYMEX market participants, and has built mission-critical AI systems for the EPA, DOE, and Boeing.

VectorCertain’s core paradigm — that AI systems do not self-authorize — represents a fundamental shift from reactive safety (detecting failures after they occur) to proactive governance (preventing failures through mathematical verification before execution). The company’s 55-patent ecosystem provides the governance layer that determines when artificial intelligence may be trusted, relied upon, or allowed to act across physical, digital, human, and adversarial domains.

About the Founder

Joseph P. Conroy is the author of “The AI Agent Crisis: How To Avoid The Current 70% Failure Rate & Achieve 90% Success,” and holds 21+ provisional patents covering AI ensemble systems and multi-model consensus technologies. His career spans deployments for Boeing, the EPA, regional power grid operators, and NYMEX trading systems, with particular expertise in safety-critical AI systems that must operate under regulatory scrutiny.

Forward-Looking Statements

This press release contains forward-looking statements regarding VectorCertain’s patent portfolio, technology capabilities, and market positioning. Patent applications are provisional filings subject to USPTO examination. Market size estimates, prevented loss calculations, and performance specifications are based on internal analysis, historical data, and prototype testing. Actual results may vary.

 

Media Contact
Joseph P. Conroy
Founder & CEO, VectorCertain LLC
Maine
www.vectorcertain.com

Assets Available for Media: Executive headshot, technology architecture diagrams, patent portfolio maps, industry-specific case studies, back-casting methodology whitepaper, and SecureAgent platform demonstrations.