Services

Three ways I help. Same person throughout.

Most AI programs die somewhere between the deck and the shipped system. I work the whole distance: picking the problems worth solving, building the thing, and staying until people use it.

Forward-Deployed Engineering

Embedded with your team, from POC to production

The distance between a promising demo and a system your business depends on is where most AI value disappears. Forward-deployed engineering closes it by putting a builder inside the business, next to the people who will use the result.

I sit with your operators and learn the workflow before writing anything. Then we build the first version fast and harden it together: data plumbing, integrations, permissions, monitoring, and the change management that gets people to actually use it.

You end up with a working system and a team that can run it without me.

  • Rapid proof of concept. A working prototype against your real data and workflow in weeks, not quarters.
  • Productionization. Integration, reliability, security review, and rollout planning so the POC becomes a dependable system.
  • Adoption and hand-over. Training, documentation, and a hand-off plan so your team owns the outcome.

AI Strategy

A plan grounded in what you can actually ship

Strategy is only useful if it survives contact with delivery. I have run PMOs, owned the budgets, and written the code, so the plan we produce is specific, costed, and buildable.

We start with your business, not with models. In a few focused weeks we map where AI removes real friction, where the data supports it, and where the risk is acceptable.

You get a prioritized roadmap, governance that fits your organization, and a small set of metrics that will tell you whether it is working.

  • Opportunity mapping. Workshops with your leaders and operators to find the use cases worth the effort, ranked by value, feasibility, and risk.
  • Governance and measurement. Decision rights, guardrails, and a small set of metrics so AI work is managed like any other investment.
  • A funded, sequenced roadmap. Quarter-by-quarter plan with clear owners, budgets, and the proof points each phase must hit.

Software Engineering

Move your engineering team to AI-based workflows

I have been writing software since the early 1990s and led engineering teams through several platform shifts. This one is bigger, and it changes how teams work day to day.

I work alongside your engineers to introduce coding agents, retrieval and evaluation pipelines, and the review, testing, and observability practices that hold quality up when output goes up.

The goal is a team that ships more, with more confidence, and understands why.

  • Agentic development workflows. Repository conventions, agent configuration, and review gates so AI-generated code meets your bar.
  • Production-grade AI features. LLM integrations, retrieval, evaluation harnesses, and guardrails built into your existing stack.
  • Security and quality by default. Testing, CI/CD, secrets handling, and monitoring set up so speed does not cost you reliability.

Questions

Questions people usually ask

Begin with a conversation

Tell me what you're trying to do. The next step can start simply.

Thirty minutes is enough to tell whether there is a fit. No deck, no pitch, just a frank conversation about your situation.