TM Tony Malott Artificial Intelligence (AI) + Regulated Systems
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Artificial Intelligence (AI) + Regulated Systems

AI architecture, automation, and regulated systems engineering from signal to scale.

I build governed systems that survive real-world constraints: manufacturing and laboratory operations, Software Development Life Cycle (SDLC) expectations, security boundaries, audit pressure, infrastructure complexity, global service delivery, human adoption, and now AI.

  • Signal to scale
  • Context-as-code
  • Manufacturing + labs
  • Model-agnostic AI
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From cable-level reality to board-level consequence: deterministic where possible, AI where useful, governed where it matters.

From cable-level reality to board-level consequence: deterministic where possible, AI where useful, governed where it matters.

Five-figure estateGlobal regulated OT and endpoint platform scope.
Multi-dozen delivery modelEngineering, operations, development, vendor, and service-provider execution.
Inspection-awareInternal and external audit support across controlled operating models.
Context-as-codeThis public CV is versioned, validated, reviewed, and deployed like software.

Operating thesis

The work is not just technical. It has to survive reality.

Regulated platforms need more than architecture diagrams. They need systems that can be operated, secured, recovered, validated, audited, handed to service providers, explained to stakeholders, and improved without losing control.

Public workbench

Public workbench

A resume can summarize scope. SharePlane shows the work: published artifacts, source posture, reusable patterns, receipts, and validation. It is broad in subject, disciplined in structure, and built so people and future tools can read without guessing.

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Progressive depth

Built from the operator's view up

I have led at enterprise scale without losing the operator's view: the cable, the endpoint, the instrument, the image, the backup, the validation package, the vendor, the user, the audit trail, and the business case.

Regulated laboratories, manufacturing, and instrument-connected systems

The niche is not just endpoint management. It is endpoint and systems engineering inside environments where instruments, production lines, laboratory workflows, validated software, quality expectations, suppliers, cybersecurity, lifecycle windows, and site reality all collide.

Builder, not AI commentator

My Artificial Intelligence (AI) work is not limited to strategy, policy, or architecture diagrams. I build and operate the patterns I advocate: coding-agent workflows, repo-governed context systems, local and cloud inference, retrieval-oriented architectures, databases, vector stores, authentication, storage layers, and governed deployment paths.

Models change. Context endures.

Models, tools, and interfaces will keep changing. The durable advantage is governed context: structured knowledge, portable memory, reusable evidence, explicit permissions, validation, and controlled handoff between human judgment and machine execution.

Tools change. Context persists.

Tools change. Context persists.

malott.ai is managed as context-as-code: a composed architecture strip keeps the durable layer visible without turning the page into a terminal theme.

  1. Human judgment
  2. Governed context
  3. Agent workflow boundary
  4. Validation and receipts
  5. Portable artifact
  6. Swappable model/tool layer

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