Operational Automation

An expensive process in back office, ops, or analyst work that people keep saying should be automated. I map it with the people who run it, measure what it costs today, and decide what should be fully automated, what needs a human in the loop, and what shouldn't be built at all. When we do build, it's with guardrails, and with a data path that can run at the edge when records can't leave your environment.

Best when

You have a real production process that might be worth automating, a team you'd rather empower than replace, and you're open to hearing that the right answer is human-in-the-loop, or no software at all.

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All engagements →

Included in every engagement

A measured baseline before any build
Training and documentation for your team
A defined handoff scoped to your readiness

What you get

Measured baseline: cycle time, cost, error rate, manual hours

Clear call on full automation / human-in-the-loop / leave manual

Data path that can analyze at the edge when records have to stay put

Hours recovered per week, tracked against baseline

Guardrails around any stochastic component

Training and handoff so your team can run it

Typical engagements

Back-office and analyst workflows where staff time is the bottleneck

Process stuck on one person's knowledge, a spreadsheet, or a brittle no-code hack

Regulated workflows where "good enough 90% of the time" isn't good enough

Human-in-the-loop workflows that keep judgment with people and push routine work off their plate

How I think about it

Consequential automation is an engineering problem before it's an AI problem. The first call is whether it should be built at all. Then whether each step wants full automation, a human in the loop, or nothing. I make those calls alongside the operators doing the work, and the goal is better work for your people, not a black box that replaces them. If the answer is to leave it alone, I'll say so.

Other engagements

AI Workflow Optimization

AI is already in production. Costs are climbing, performance is uneven, or one frontier model is doing every job. I audit the workflow, run evals task by task, and hand back a system that costs less and holds up better. You keep the eval suite.

AI & Automation Readiness

You want to automate, but data is scattered, systems don't talk, or AI shipped with no harness. You need clean accessible data, addressable systems, guardrails around anything stochastic, and a data path security can stand behind.