Processes - AI Engineering Enablement - Onboarding & Delivery Process

Overview

This process is for software teams that want to improve how AI-assisted development actually works in practice.

The goal is to reduce vagueness early, assess where the current workflow is strong or weak, then improve the operating model through a practical delivery rhythm tied to real work.

1 - Interest and intake

The process starts with the dedicated service intake form.

The intake captures:

• The team and product context
• The current AI usage pattern
• The main quality, review, or workflow pain points
• The current tooling and issue-tracker setup
• The improvements the team most wants to see
• The intro call slot that works for you from the currently available options

2 - Fit review, rate confirmation, and intro call

After the intake is submitted:

• I review the engagement for fit, timing, and likely workflow shape
• I confirm whether I have current capacity to support the work
• If it looks like a fit, I reply with the appropriate rate and confirm the intro call
• If it is not a fit, or the timing is wrong, I say that clearly and suggest the most useful next step

3 - Workflow assessment and first-slice planning

If we both want to proceed, the next step is to understand the current delivery environment well enough to choose the right first improvement slice.

That usually includes:

• Reviewing the current delivery workflow and AI usage patterns
• Looking at the current repo, standards, and documentation surface
• Identifying where friction, review overhead, or drift is showing up most clearly
• Deciding which workflow area should be improved first

4 - Kickoff and initial implementation

The first phase focuses on creating visible improvement quickly without trying to redesign everything at once.

Typical early work includes:

• Tightening specifications, tasking, or review structure
• Improving repo guidance, rules, or context surfaces
• Introducing stronger validation or review loops
• Improving onboarding, ticket handling, or local development flow where it matters most

5 - Ongoing operating rhythm

Once the engagement is underway, the work runs in a practical rhythm:

• The current improvement focus stays visible
• Real delivery examples are used rather than hypothetical cases only
• Changes to workflow, guidance, or supporting setup are tested in practice
• Risks, blockers, and decisions are surfaced early
• The next useful improvement slice stays clear so momentum does not stall

6 - Decision points during the engagement

As the work progresses, a few decisions tend to come up:

• Continue improving the same workflow area more deeply
• Expand into another area such as onboarding, bug flows, or PR review
• Increase or reduce the level of embedded support
• Shift into a broader engineering engagement if that becomes the better fit
• Wrap after the core operating-model improvements are in place

7 - Offboarding or extension

At the end of a phase, we either continue with the next improvement priority or close cleanly.

Closeout normally includes:

• A clear record of what changed
• Handover on any active workflow or operating-model work
• Notes on what to continue internally
• Recommendations for the next most useful improvement steps