Most AI systems fail at execution. I build the control planes that make them accountable.
Control-Plane Architect for AI Systems
Building real-time decision systems, agentic infrastructure, and correctness frameworks.
I work on systems where AI must act, not just predict — trading, data integrity, and on-device intelligence.
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AI Decision Infrastructure
Bounded-latency decision loops, evaluation pipelines, and observable execution systems -
Agentic Control Planes
Systems that coordinate tools, workflows, and outcomes with measurable behavior -
Correctness & Data Integrity (ASA/DCML)
Architectures for verifiable storage, arbitration, and recovery under failure -
On-device AI Systems
Swift/SwiftUI + CoreML pipelines for privacy-preserving, real-time inference
Real-time decision system using multi-signal consensus and regime-aware execution
→ https://phoenix.industriallystrong.com
Dual-chain mirrored ledger for verifiable storage and recovery
→ https://industriallystrong.com/lab
Labs, experiments, and system proofs across AI, storage, and physical computation
→ https://industriallystrong.com
- Reczipes3 — OCR → structured extraction → LLM reasoning
- KanjiKanaTrainer — on-device handwriting recognition (PencilKit + CoreML)
- KeepTrack — iOS system for structured daily tracking
- https://industriallystrong.com
(Control planes, AI systems, correctness, and physical computation)