AI-Native Engineering Enablement

Three ways to work together.

Start with the AI Coding ROI Readout: a fix diagnostic that finds where your AI-coding spend is leaking, benchmarked against independent research. From there, the deeper engagement that installs the fix, and a standalone workshop for the house style that makes it stick.

Book a discovery callSee the proof →

Ex-Amazon · Ex-The New York Times · Prices published below

01 · Engagements

Pick the smallest thing that answers your question.

Fixed scope, published pricing, artifacts you keep. No hourly meters, no hidden fees.

Start here
~2 weeks · mostly async

AI Coding ROI Readout

$4,500
3 tiers · Startup $2,500 to Extended scoped

A 2-week diagnostic that finds exactly where your system is leaking the AI spend (review bandwidth, workflow, guardrails), and the on-ramp to installing the fix. You keep a board-ready deck and a keep, renegotiate, fix, or cut recommendation.

  • Adoption and throughput data pull
  • Benchmark vs. independent medians
  • Spend versus value, by team
See the full breakdown →
3-week engagementOnboarding founding clients

AI-Native Delivery Accelerator

$7,500
3 tiers · Startup $5,000 to Enterprise scoped

The deeper engagement the Readout points to: install the operating-model fix and prove it on one team and one repo, with a before-and-after number.

  • Week 1 · Diagnose
  • Midpoint · Half-day readout
  • Weeks 2 to 3 · Execution support
See the full breakdown →
90 minutes, live · remote or onsite

CLAUDE.md Workshop

$3,000

A live 90-minute working session that encodes your team's standards into your real repositories, so coding agents follow the way you already ship: context files, review habits, and workflows tuned to your codebase.

  • A live 90-minute working session, on your repos
  • Your CLAUDE.md files, written live
  • Review habits your team will keep
See the full breakdown →

02 · Process

How it works.

  1. 01

    30-minute discovery call

    Free, no pitch. Your setup: seats, tools, team size, who owns the rollout, and where it's stalling. You'll leave with something useful either way.

  2. 02

    Pick the engagement

    I propose the smallest engagement that fixes the problem and name the price on the call. No fit, no proposal: you get a one-page write-up of what I saw instead.

  3. 03

    Fixed scope, fixed dates

    Every engagement has an end date set at kickoff. Async support runs in a shared Slack channel with next-business-day responses.

  4. 04

    You keep the artifacts

    Decks, recommendations, templates, recordings. Everything is built to run without me after the engagement ends.

03 · Fit

Who this is for.

You meet me on the call and you work with me on the engagement. There is no bench and no handoff.

A fit

  • Engineering managers, directors, and Heads of Developer Experience or Platform with budget to run a rollout, with VPs and CTOs at the larger end
  • Engineering orgs of roughly 20 to 500 engineers, sweet spot 20 to 200
  • Teams with seats already bought and adoption stalling, or a rollout about to start
  • Leaders who want standards and governance, not another tool demo

Not a fit

  • Company-wide “AI for every department” transformation. I stay in the engineering lane; I’ll point you elsewhere or scope it down to your eng org.
  • Individual engineers. This practice is built for teams; the writing and the free toolkit are the best place to start solo.
  • Anyone shopping for a 10x productivity promise. I anchor to measured adoption data, and the honest numbers are the ones that survive your CFO.

Bring your adoption numbers.

The call is 30 minutes and free. I’ll name the smallest thing that would move your numbers, even when that thing isn’t hiring me.

Book a discovery call
alextongme@gmail.com →

I reply within one business day