AI Engineering Enablement · Strategy and diagnostic offers

Adopt AI without multiplying weak engineering practices.

AI can increase engineering capacity. It can also scale vague work, bad architecture, and weak review faster than your organization can absorb the damage. The operating model determines which result you get.

Book a 20-minute fit call → See what is included
When this fits

The problem is active, visible, and expensive.

01
Developers are already using AI, but every team has different tools, rules, and review expectations.
02
Leadership cannot distinguish real productivity gains from faster production of fragile code.
03
Security policy exists, but the engineering operating model does not.
04
Stories, architecture context, tests, and code standards are too weak to guide reliable AI output.
05
The organization needs a 2026 AI plan, but another broad strategy presentation will not change delivery.
The engagement

Move from scattered tool use to a controlled engineering capability.

01

Current-state review

Map how teams use AI today, which tools and workflows matter, where risk concentrates, and which claims leadership cannot yet measure.

02

Engineering controls

Assess task definition, context, standards, architecture guidance, reviews, tests, deployment gates, costs, and feedback loops.

03

Pilot operating model

Select the highest-value pilot workflows and define the standards, measures, owners, and rollout sequence for the next 90 days.

What leadership receives

A decision-ready plan, not a slide deck that dies in a folder.

01
A map of current AI usage, tools, workflows, risks, and ownership gaps.
02
A prioritized list of pilot workflows tied to measurable engineering outcomes.
03
Recommended standards for task definition, context, code quality, review, and deployment.
04
A measurement plan covering speed, quality, rework, reliability, adoption, and cost.
05
A written 90-day enablement plan with owners, gates, and rollout decisions.
06
An executive readout that separates tool choices from the operating model required to use them well.
Ways to start

AI Engineering Strategy Session

$1,500

A pre-session questionnaire, a 90-minute leadership working session, and a written decision memo covering workflows, tools, standards, governance, and measurement. The fee is credited toward a larger engagement.

AI Engineering Enablement Diagnostic

$5,000 to $7,500

A ten-business-day review of current usage, standards, stories, reviews, tests, deployment gates, costs, and pilot opportunities, followed by a written 90-day plan.

A good fit
  • Engineering teams are already using AI or leadership expects adoption this year.
  • A real decision about tools, standards, pilots, measurement, or rollout must be made.
  • Engineering leadership will own the outcome rather than delegating it entirely to procurement or security.
  • The organization wants disciplined adoption tied to delivery and quality results.
Not what this is
  • General prompt training or company-wide AI literacy workshops.
  • Sales, marketing, or back-office automation consulting.
  • Arbitrary chatbot development or outsourced application delivery.
  • Legal compliance advice presented as engineering governance.

The fit call is twenty minutes. The next step is specific.

We will establish the problem, urgency, decision owner, scope, and whether this engagement is the right paid next step.

Book a 20-minute fit call →