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.

Best when

You want AI or automation and the foundation isn't there yet: messy data, siloed systems, or AI you've shipped with nothing verifying it.

Want to pressure-test fit quickly?

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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

Data cleaned, modeled, and accessible for what comes next

Siloed systems addressable by people and agents

Evals, observability, and guardrails around existing AI

A stated data path: where records live, how they move, where analysis runs

Org can automate without depending on me

Typical engagements

Data consolidation across systems that don't talk

Integrations that make internal systems automatable under your security constraints

Evals, observability, and guardrails around AI that shipped without them

Readiness assessments, including edge processing when data has to stay put

How I think about it

Most AI fails at deployment, not at the model. The data isn't accessible, the systems can't be reached, and nothing checks that a stochastic component is still doing its job next month. I fix that foundation, with a data path security can stand behind, so the org can automate on its own.

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.

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.