About / Justin Haut
Built by an engineer who works inside the process.
Process to Production is the consulting practice of Justin Haut, an AI and process automation engineer who builds complete systems around operational work.
The through line is simple: understand how the work really moves, make the control points explicit, and use software or AI only where it creates durable leverage.
Operating range
From a plant floor to a household ledger.
Different domains, same engineering problem: important work held together by fragmented data, manual judgment, and hidden rules.
Manufacturing + ERP
Manufacturing, ERP, and trade compliance: duty recovery running in production, a controlled procedure collapsed to a single screen, engineering document analysis, part risk, and the cloud platform underneath all of it.
Property operations
A multi-role field-operations platform that has evolved in production for nearly four years across turnovers, inspections, dispatch, vendors, and reporting.
Financial product
A full-stack mobile finance product spanning bank data, transactions, accounting logic, planning, secure AWS infrastructure, and human-reviewed AI assistance.
Why this practice exists
Software should leave the operation stronger than it found it.
Justin currently owns the enterprise application platform for a multi-entity manufacturer: the ERP applications themselves, the cloud infrastructure that extends them past what an ERP can hold open, the AI pipelines that read their documents, and the identity and deployment tooling underneath.
That work sits alongside years spent building and operating full products: a property-operations platform refined under real field use, and a personal-finance mobile application with its own production AWS backend.
All of it was full-stack work before the current generation of models existed—interfaces, data models, infrastructure, and the unglamorous operational middle. Agent tooling changed the speed of that work, not the standard it is held to: the leverage only holds up because the tests, harnesses, and deployment gates underneath it do.
The result is a practice that can move from process discovery to interface, integration, data model, infrastructure, AI boundary, deployment, and ongoing operations without losing the original business problem between handoffs.
Working principles
Practical by design.
Start with the process
The useful architecture is hidden in the real handoffs, exceptions, decisions, and economics of the work.
Keep control explicit
Models can interpret and assist. Deterministic logic should own money, policy, validation, and irreversible actions.
Build for the second year
Production software earns trust through observability, failure handling, maintenance, and iteration—not the first demo. That includes what it costs to keep running.
A useful first conversation