Industrial systems architect Production AI operator Boerne, Texas · USA

Industrial systems architect. I deploy AI and enterprise systems into physical operations, and they hold.

Mechanical engineer (B.S., summa cum laude) · 5 years inside a robotics manufacturer · production AI operator

I find expensive, failure-prone operational work and turn it into governed software: configuration systems, physical-AI infrastructure, auditable data products, and tools that expose failure.

Physical engineering background Production data and AI systems in Python Integrations across disparate enterprise systems Secure, permissioned environments Live incident response and root-cause analysis
estimated~200hours

estimated manual package assembly removed per machine*

modeled$43K-56K

modeled annual capacity recovered at current run rate*

33.5K

line production application footprint

243

commits across a 5.5-month solo build

*Owner-provided prior-effort inputs. Financial impact is a modeled capacity estimate, not audited savings.

A production operating system for a configurable industrial machine.

Not a demo. Not a dashboard over a spreadsheet. A governed system that changed how Product, Engineering, external builders, and Operations hand work across the InductOne lifecycle.

Production system · Private implementation

InductOne Tools

ERPNext / Frappe configuration, builder handoff, and installed-base operations

ROLEOwner / ArchitectINDUSTRIAL ROBOTICS
BEFORE / CROSS-FUNCTIONAL SCRAMBLE

Product and Engineering manually assembled every builder package. The estimated 200 hours of prior effort came from constrained senior work per machine.

AFTER / GENERATED FROM GOVERNED STATE

Each package is generated for the selected configuration. Product and Engineering package-assembly effort approaches zero. The same engine now produces RFQ packages.

SYSTEM SPAN

  1. 01Configuration
  2. 02Engineering gates
  3. 03Builder package
  4. 04As-built acceptance
  5. 05Installed base
40

server-side API methods enforcing process rules

~50

custom DocTypes across the build lifecycle

24

sequential, idempotent migration patches

11

roles, including row-scoped external builders

WHAT THE CODE ENFORCES
  • Configurable, hierarchical BOM builds and repo-governed snapshots
  • Engineering signoff, release gates, and controlled change
  • External-builder permissions scoped to assigned machines
  • Atomic serial allocation and as-built acceptance
  • Installed-base records and compatibility-aware service operations
  • Migration, test, deployment, and sandbox discipline
THE THESIS“Providing a builder’s build package was a cross-functional scramble. It is now zero manual package-assembly effort.”
DEPLOYMENT, NOT JUST DESIGN

The platform serves engineering, production, supply chain, and external contract builders under row-scoped permissions. Rollout ran through 24 idempotent migration patches and a twice-migrated candidate sandbox before production. I hold the Operations sign-off on the product's software releases: every requirement traced to linked evidence before approval. I also support it live: diagnosis runs on records, snapshots, and diffs against the system of record. Teams route through that gate willingly: the no is always evidence, never opinion, and resolutions are worked jointly with the owning team.

Evidence note. Footprint figures are measured from the private repository. The estimated 200-hour prior process and compensation inputs are owner-provided. The modeled $43K-56K annual figure assumes four machines. It is not an audited realized-savings claim.

The same operating pattern, under different constraints.

The portfolio is broad on purpose. The through-line is making ambiguous systems legible, enforceable, observable, and honest.

02

Public-interest measurement system

OnScript

Live · Public beta

A daily, symmetric measurement instrument for Congressional language, with receipts. It separates deterministic measurement from bounded AI interpretation. It publishes its methodology and exposes operational state.

285 Python files · 134 test files · 7 scheduled workflows

Python · Data systems · LLM boundaries · Observability

03

Economic telemetry laboratory

The Exhaust

Open source · Research

A collection of public-data collectors and preregistered retrocasts built to test whether overlooked physical-world exhaust can say something useful about the economy. Failed hypotheses stay visible. The point is not a prettier forecast. It is a more falsifiable one.

192 files · 21 test files · 12 automation workflows

NHTSA · CMS · WARN · CPSC · Reproducible research

04

Deterministic safety tripwire

Agent Guards

Open source · Packaged

Deterministic tripwires that produce reproducible evidence for known patterns. They check agent inputs, files, diffs, email, and packages. The system ships as an MCP server, API, and installable plugin. It is a tripwire, not a comprehensive security product.

598 repository files · MCP + API + plugin · 32/40 public corpus cases caught

TypeScript · Python · MCP · Security tooling · CI

ForgeSense

An industrial vibration and edge-monitoring testbed that closes the loop between fixture, sensor, embedded compute, analysis, and operator interface.

ADXL355 · Raspberry Pi · Python analysis · Go edge service · React / TypeScript

Founder · Experimental · Private laboratory system

  1. 01Physical rigrepeatable excitation
  2. 02Sensor edgeADXL355 + Pi
  3. 03Analysisfeatures + statistics
  4. 04Interfaceoperator evidence

AI in production, governed

I run multi-agent AI workflows on business-critical work daily. The reason they can be trusted is not the models. It is the structure around them: orchestration and audit separated from implementation, output reviewed like any production change, claims verified before anything ships. That review has caught embedded PII in a distributable file, staging data presented as production, and access-control claims marked verified that were never tested. Nothing ships on assertion.

Method: how a deployment runs

  1. 01

    Observe the real work

    Start with the physical process, the operators, and the ugly handoffs. Do not start with a software feature list.

  2. 02

    Model states and invariants

    Turn tribal knowledge into explicit configuration, lifecycle states, permissions, and rules that survive scale.

  3. 03

    Encode the guardrails

    Put release gates, role boundaries, provenance, and failure handling on the server side where they are enforceable.

  4. 04

    Instrument the truth

    Expose operational state, preserve failed hypotheses, and separate measured evidence from estimates and interpretation.

  5. 05

    Ship the whole system

    Own migrations, tests, deployment, documentation, recovery paths, and the interface people use.

This is the same loop whether the user is my own plant floor or someone else's: discover the real workflow, encode its constraints, integrate with the systems that already exist, instrument the truth, and stay accountable after go-live.

I am most useful where physical operations, enterprise systems, and AI meet.

I work inside an industrial robotics company and build production software beyond my formal lane. My advantage is not “knowing some code.” It is being able to see the operating system hidden inside a physical process. I own the technical and organizational work required to make it real.

I am exploring remote roles in manufacturing systems, enterprise applications, industrial AI, and physical-AI deployment.

BASED
Boerne / San Antonio, Texas
EDUCATION
B.S. Mechanical Engineering, UT San Antonio, summa cum laude, 4.0
WORK STYLE
Cross-functional · owner mentality

Open to senior remote roles deploying AI and enterprise systems into physical operations.

Need someone who can cross the boundary?

Start with the work. Then let’s talk about the system behind yours.