Your team is using AI. Nobody designed the workflow.
I help engineering leaders embed AI into how their teams build, review, and ship — not just what tools they install. The difference between ad-hoc prompting and a system that scales.
30 years building enterprise systems. Now focused on AI operating models for engineering teams.
Sound familiar?
Every engineering leader hits these walls once AI adoption moves past the early adopter phase.
These are not tool problems. They are operating model problems.
Four areas where I help engineering leaders.
Not tool recommendations. Systems thinking applied to AI adoption.
AI Workflow Design
Design development workflows where AI agents handle execution and engineers focus on judgment. Move from ad-hoc prompting to structured, repeatable processes.
Agent Orchestration
Structure multi-agent systems, prompt engineering at scale, and review patterns that keep humans in control. Build the coordination layer between AI capabilities and your engineering standards.
Developer Leverage
Identify where AI multiplies engineering output — code generation, content pipelines, testing, documentation — without adding risk or eroding code quality.
Enterprise AI Adoption
Help leadership navigate AI integration with governance, security boundaries, and measurable ROI. Strategy that survives contact with compliance.
From assessment to operating system in weeks.
Not a 6-month transformation roadmap. Concrete changes your team can adopt immediately.
Discovery & assessment
I learn how your team builds today — tools, review processes, deployment patterns, and where AI is already being used (or avoided). This identifies the highest-leverage insertion points.
Typically 1-2 sessions with your tech leads and a review of your development workflow.
Design the operating model
I design the AI-augmented workflow — which decisions stay with humans, where agents execute, how review works, and what quality gates exist. This becomes a documented system your team can follow.
Deliverable: a concrete playbook, not a slide deck. Includes prompt templates, review checklists, and measurement criteria.
Implementation & handoff
I work with your team to implement the system in real sprints with real code. Not a theoretical exercise — we build the patterns together until the team can run them independently.
I stay engaged until the team is self-sufficient. Training and documentation included.
I build with AI every day. That is the difference.
Most AI consultants advise from the outside. I ship production code using AI agents daily — multi-agent orchestration, automated testing, content pipelines, and full-stack applications built with Claude Code and custom tooling.
When I tell your team how to structure AI workflows, I am describing systems I have already built and battle-tested. Not theory — practice.
Read my technical writingCommon questions
What engineering leaders ask before we work together.
What does "AI workflow design" actually mean?
It means designing the operating model — not picking tools. Which decisions stay with humans? Where do agents execute? How does review work? How do you measure output quality? I help your team answer these questions with a repeatable system, not ad-hoc experimentation.
We already use Copilot/ChatGPT. What more is there?
Individual developers prompting AI is table stakes. The real leverage is at the system level — structured multi-agent workflows, prompt engineering at scale, quality control patterns, and governance that lets your team move fast without introducing risk. That is the gap between "we use AI" and "AI is embedded in how we operate."
How do you work with teams? Is this a one-time engagement?
It depends on the scope. Some teams need a focused 2-4 week audit and implementation plan. Others want ongoing fractional leadership to guide adoption over 3-6 months. I structure engagements around outcomes, not hours.
What size teams do you work with?
Typically engineering organizations with 10-200 developers. Large enough that ad-hoc AI adoption creates inconsistency, small enough that a single experienced leader can move the needle.
Do you write code or just advise?
Both. I have 30 years of hands-on engineering experience and I still ship code daily using AI agents. I can design the strategy AND demonstrate it working in your codebase. That credibility matters when asking engineers to change how they work.
What if our leadership is skeptical about AI ROI?
I build governance frameworks and measurement systems alongside the technical implementation. Your leadership gets security boundaries, compliance alignment, and measurable productivity data — not just demos. Strategy that survives contact with the CFO.
How is this different from hiring an AI consultancy?
Consultancies send junior associates who have never built production systems. I am a senior engineer who has led enterprise teams for three decades and builds with AI agents every day. You get the strategist and the practitioner in one person.
What does a discovery call look like?
A 30-minute conversation where I learn about your team structure, current AI usage, and the outcomes you are trying to achieve. No pitch deck. No sales process. Just a direct conversation about whether I can help.
Find out where AI fits in your engineering workflow.
A 30-minute assessment of your team's AI adoption, current workflow gaps, and the highest-leverage insertion points.
You will leave with concrete recommendations, not a sales pitch.
Request an AI Workflow AuditFree 30-minute assessment. Concrete recommendations. No obligation.