Engineering teams

Your developers use AI. Your engineering process doesn't, yet.

From AI-assisted developers to an AI-native engineering team. Most teams have already bought Copilot, Cursor, or Claude Code. Individual developers feel faster. Team-level delivery has not moved.

I help engineering teams close that gap. I start by measuring where you are, then build a staged plan: quick wins first, experiments clearly separated from org-wide rollout. Then I help implement it, and the team owns it afterward.

Why delivery has not moved

  • Personal setups

    AI runs on personal subscriptions and personal sessions, so nothing is shared, repeatable, or governed.

  • No workflow change

    Agents are not wired into PR review, CI, incident response, or ticketing, so the gains stay at the keyboard.

  • Silent failures

    Agents miss context or drop content without throwing errors. That is dangerous in places like incident response.

  • No measurement

    Leadership cannot see whether the spend is paying off.

Where the team is

I assess where you are and move you up a level, with measurable results at each step.

  1. 01

    Assistive

    Developers use AI in the editor.

  2. 02

    Agentic

    Agents handle defined tasks such as PR reviews, test generation, and ticket triage, with humans approving.

  3. 03

    Autonomous

    Agents investigate and fix issues triggered from Slack, Linear, or CI, with guardrails.

How to start

  • Fixed fee, about 2 weeks

    Engineering AI Assessment

    A maturity baseline, a DORA and PR cycle-time baseline, and a review of tooling, identity, secrets, and security. You get a phased roadmap that separates quick wins, experiments, and org-wide rollout.

  • Fixed scope

    Implementation

    I set up shared agent infrastructure and an org identity in place of personal subscriptions. I wire agents into PR review, CI, and incident workflows with guardrails, and run hands-on sessions so the team owns it.

  • Monthly

    Ongoing advisor

    I track the metrics, evaluate new tools as the landscape shifts, and expand to the next maturity level.

What I measure

I commit to measuring these. I do not promise a specific number.

  • DORA metrics: deployment frequency, lead time, change failure rate, and recovery time
  • PR review cycle time
  • Incident time-to-resolution
  • AI spend per engineer

A SaaS company

Recently, a single review of a SaaS company's AI stack cut their AI running costs in half.

I have led an engineering organization's move toward agentic workflows. That is my experience, not a client story. I have managed engineering teams, and I have done this work as a principal engineer at Intel, Qualcomm, and Dapper Labs.

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