Enterprise AI Trust Infrastructure Portfolio
Patent-backed systems for deterministic control, replay, provenance, remediation, and runtime governance.
My independent AI trust infrastructure work focuses on the control layers enterprises need between AI systems and real-world decisions: authorization gates, replayable decisions, provenance trails, deterministic remediation, and runtime execution controls.
These systems are designed around one thesis: enterprise AI will not scale safely through model evaluation alone. It needs inspectable, replayable, policy-bound infrastructure at runtime.
IP Portfolio Status
Public-safe summary of the independent AI trust infrastructure patent portfolio.
| System | Public Status |
|---|---|
| Deterministic Code Authorization | U.S. patent application with Notice of Allowance; issue fee paid; awaiting patent number |
| Deterministic Offline Code Remediation | Filed continuation-related utility application |
| Decision Replay & Ledger-Verified Execution | Filed U.S. utility application |
| Cross-Modal Provenance | Filed U.S. utility application |
| Runtime Execution Control Layer | Application in preparation |
These systems are presented at a public-safe level. Detailed claim mappings, implementation notes, and deeper architecture walkthroughs are reserved for relevant product, platform, research, and IP conversations.
How the systems fit together
The portfolio is organized as a five-system enterprise AI trust stack. Each system addresses a different control point in the AI deployment lifecycle: authorizing code, remediating findings, replaying decisions, preserving provenance, and controlling runtime execution.
Deterministic Code Authorization
Verifies whether AI-assisted code changes should be approved, blocked, or escalated.
Deterministic Offline Code Remediation
Produces replay-verifiable, template-based remediation outputs for code findings.
Decision Replay & Ledger-Verified Execution
Reconstructs AI-assisted decisions using captured inputs, rule paths, hashes, and audit records.
Cross-Modal Provenance
Tracks artifact lineage across AI-generated and AI-transformed outputs.
Runtime Execution Control Layer
Applies policy gates before AI-driven actions execute in enterprise workflows.
Five-System Trust Stack
Deterministic Code Authorization
U.S. patent application with Notice of Allowance; issue fee paid; awaiting patent number
Focus: AI-assisted code authorization and policy-controlled release gates
Problem
AI-assisted development workflows can generate, modify, or recommend code changes faster than traditional review systems can verify them. Enterprises need deterministic evidence before merge or deployment.
Solution
A replay-verified authorization layer that evaluates code changes against deterministic rules, policy constraints, evidence logs, and deployment gates.
Why It Matters
It turns code approval from a subjective review step into an evidence-backed control point for enterprise software delivery.
Architecture Flow
Deterministic Offline Code Remediation
Filed continuation-related utility application
Focus: Ledger-verified replay and template-based patch generation
Problem
AI-assisted remediation can produce plausible patches, but enterprises still need reproducible, auditable, and policy-bound remediation paths before code changes are trusted.
Solution
An offline remediation system that maps findings to deterministic remediation templates, records evidence in a ledger, verifies replay consistency, and produces reviewable patch outputs.
Why It Matters
It creates a safer bridge between code findings and code fixes by making remediation reproducible, explainable, and auditable.
Architecture Flow
Decision Replay & Ledger-Verified Execution
Filed U.S. utility application
Focus: Reconstructable AI-assisted decisions and audit reports
Problem
Enterprise teams often cannot reconstruct why an AI-assisted system made or recommended a decision after the fact.
Solution
A deterministic replay and ledger-verification layer that records input snapshots, rule paths, hashes, and decision traces.
Why It Matters
It enables auditability, incident review, compliance support, and governance for AI-influenced decisions.
Architecture Flow
Cross-Modal Provenance
Filed U.S. utility application
Focus: Artifact lineage across AI-generated and AI-transformed outputs
Problem
AI-generated artifacts increasingly move across text, documents, code, images, and workflow systems without clear lineage.
Solution
A provenance layer that tracks artifact origin, transformations, model/tool involvement, metadata, and downstream usage.
Why It Matters
It gives enterprises a way to understand, verify, and govern AI-influenced content across modalities.
Architecture Flow
Runtime Execution Control Layer
Application in preparation
Focus: Approve, block, or escalate AI-driven actions before execution
Problem
Most AI governance happens before deployment, but real risk appears when AI outputs trigger downstream actions.
Solution
A runtime control layer that applies deterministic policy checks before AI-driven actions are executed in production or enterprise workflows.
Why It Matters
It provides a practical control plane between model outputs and real-world consequences.
Architecture Flow
Product Leadership Operating Model
The systems are technical, but the operating model is product-led: define the enterprise risk, design the control surface, specify the evidence path, and measure whether teams can deploy AI with greater confidence.
1. Problem Framing
Translate ambiguous AI risk, governance, and reliability problems into clear enterprise platform requirements.
2. Platform Strategy
Design reusable control layers across authorization, replay, provenance, remediation, and runtime execution.
3. Execution System
Define architecture, evidence flows, system boundaries, policy paths, and implementation-ready product requirements.
4. Adoption & Measurement
Connect platform capabilities to measurable outcomes: reduced review ambiguity, improved audit readiness, safer deployment paths, and faster incident investigation.
Why this matters for enterprise AI
Most AI governance conversations focus on policies, evaluations, and model behavior. Those are necessary, but not sufficient. Enterprises also need runtime infrastructure that can verify decisions, preserve evidence, reconstruct outcomes, and enforce policy before AI-driven actions affect software, workflows, customers, or regulated operations.
Model evaluation is not enough
Pre-deployment evaluation does not guarantee safe behavior across real enterprise workflows.
Runtime control is the missing layer
Enterprise AI needs policy gates, replay paths, audit evidence, and escalation logic during execution.
Trust requires evidence
The future of AI adoption will depend on whether decisions can be inspected, replayed, governed, and explained.
Interested in the deeper architecture?
I keep public pages intentionally high-level to protect active IP and avoid exposing claim-level implementation details. For senior AI product, platform, trust infrastructure, governance, research, or recruiting conversations, I can share a private walkthrough of the product thesis, system boundaries, and public-safe architecture details.