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.
Best when
AI is already live and the bill, the latency, or the accuracy has become the problem. You'd rather make the next model decision on evidence than on whichever one was easiest to reach for.
Want to pressure-test fit quickly?
Schedule a CallIncluded 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
Typical engagements
How I think about it
One frontier model for every task is the common, expensive mistake. Right model per task is a measured cost, latency, and accuracy tradeoff. I make that measurable and leave you the eval suite so the decision still holds as models and prices change.
Other engagements
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.
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.