Active Builds
Five systems I'm building and operating on my own time — governed knowledge architecture, local AI inference infrastructure, compliance-grade document pipelines, a legal processing and routing framework, and an enterprise governance compendium. The problems they solve are the same ones I see inside enterprise environments.
A 550+ document personal vault built to enterprise content management standards. ISO 27001-aligned data lifecycle policy — nine review classes enforced at every git commit via pre-commit hooks that block any active file missing classification. Multi-lane routing: legal and sensitive content stays strictly local; cloud-eligible content routes separately. Version-controlled with a formal dev → test → prod branch promotion protocol. The vault is the authoritative source; everything downstream — AI-generated enrichment, vector indexes, exports — is derived and read-only.
Same governance architecture I'd build for any organization that needs to trust what it knows.
NVIDIA Quadro P5000 (CUDA) and AMD Radeon Pro WX 9100 (Vulkan) — two independent inference lanes on a single workstation running a structured five-seat model council. Each seat is validated through 4-hour soak testing before production admission: 2,800+ inference cycles, zero failures, VRAM telemetry logged throughout. Seat types span dense models (7B–30B parameters), MoE architectures, and a vision seat for image annotation and document OCR. An 80% VRAM ceiling is the only admission rule — no exceptions, no override.
I know the difference between what documentation claims and what a model delivers under sustained load — because I ran the test.
vault_enrich.py converts binary vault files — PDF, DOCX, XLSX, images — into structured knowledge records: governed YAML frontmatter with semantic fields (who / where / when / why / key_facts / semantic_tags) plus an immutable source snapshot. First production batch: 48 enrichment outputs and 4 vision-annotated context records, all human-reviewed. Sensitive documents are quarantined before entering the pipeline and routed to the compliance handler. Idempotent by design — re-enrichment runs against the frozen source extract, not the original file. A degraded model pass cannot corrupt a clean record. Downstream: Qdrant vector indexing for semantic search across the full knowledge base.
Same pattern as any enterprise enrichment pipeline — the difference is I own the failure modes.
GPRF is the master orchestrator for all document ingestion requiring sensitivity controls. Every file is classified before any AI council call — legal, restricted, and privileged content never touches a cloud LLM. Pre-determination policy gate: classification happens at entry, not after. Built to exceed LKIF (Legal Knowledge Interchange Format) and SALI LMSS standards: immutable chain-of-custody audit trail, privilege flags, jurisdiction tracking, and OQ-3 confidence calibration gates on every council output. Tokenization replaces PII and identifying information before inference; rehydration restores it after. Encrypted LUKS storage at rest. The full pipeline — tokenize, dispatch, rehydrate, audit — is sequenced and auditable end to end.
This is the compliance architecture I'd propose for any organization handling regulated or legally sensitive content at scale.
Large organizations don't fail from lack of methods — they fail because decision integrity degrades under scale before execution visibly collapses. Lean, Agile, VAVE, and OpEx each work correctly within their bounded environments. None were designed to govern enterprise-scale decision load collectively. At scale, these methods interact without a unifying control system — decisions move off-system, lose rationale, drift from accountable authority. Capital-Grade Governance addresses that specific failure mode: a control architecture that constrains methods within a shared governing structure, preventing mutual interference without replacing any of them.
The compendium is benchmarked against seven Big Strategy firm models — Accenture, Deloitte, EY-Parthenon, KPMG, McKinsey, PwC, and MBB — with a differentiation map showing where each approach fails under sustained transformation load. Formal interfaces for Lean, VAVE, Agile, OpEx, and JCIT. An AI Governance Interface mapped to the EU AI Act (Regulation 2024/1689) and an ISO 42001 alignment layer for AI management systems. Governance enforced through three gate types — Capital Release, Authority, and AI Output Boundary — acting before capital is irreversibly exposed, not after. Currently in Release Candidate 2 development. Eleven validated change proposals incorporated to date. A formal JCIT Stress Assessment run against the framework's own structure.
If transformation methods in your organization are proliferating faster than your governance can manage them collectively — this is the architecture built for that problem.
These aren't side projects. They're a working proof of how I approach complex, sensitive, multi-system problems — with the same rigor I'd apply inside any enterprise: governance before feature, compliance before speed, test it under real conditions before you trust it.
I'm building infrastructure that most organizations would staff a team to design. I'm building it alone — which means I understand every layer, every failure mode, and every tradeoff.