The pattern outside engineering
Engineering was the obvious place to begin with AI: digital inputs, text outputs, fast feedback. After six months, the useful question became whether the same practices could move into the rest of the company.
Much of the operations work had a similar shape. There was a large amount of reading, a repeatable set of checks, and consequential judgment in the middle.
The system
We built an internal MCP server that exposed customer records, title workflows, document templates, and checklists to an AI interface. The system understood the company’s vocabulary and could inspect the same operational context a person would normally gather by moving across several tools.
The valuable use cases were concrete:
- Read the queue and help operators identify which files were most likely to fall apart today.
- Run a first-pass review for missing fields, documents, or conflicting dates.
- Walk the mechanical parts of a closing-readiness checklist before a signing.
The human boundary
The AI did not replace the operator’s judgment. It shortened the list of things that required that judgment.
Senior operators still made the final call. The system handled patient, high-volume reading and surfaced the places where experience mattered. The guardrails defined which actions were informative, which needed confirmation, and which remained entirely human.
The wider lesson
The durable asset was not access to a model. It was the context, the constraints, and the decisions about where autonomy stopped. AI was useful because it was embedded in a real workflow without pretending the workflow no longer needed people.
