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Impact · Selected engagement

One week to make the team twice as fast. Six more to build what most teams can't.

JENGAI had real customers and a round closing. In a single week alongside the team, I doubled how fast each engineer shipped and left them an AI workflow they own. Then I spent the next six weeks on the hard part: clearing the performance ceiling they had hit, and building a real AI capability into their product itself, the kind of integration most teams don't yet know how to attempt.

What began as a weeklong intensive became an ongoing engagement. The week proved it, then Shayne kept me on to build the rest.

~2x
Faster per engineer
Output per person, from one week of work, and it held all month.
~70%
Of the AI work, run by them
The delivery system I installed, adopted across eight engineers, not just me.
13m → 15s
A core read, fixed
One of several order of magnitude wins the team could not crack alone.
~2 to 3x
Annualized return
Added engineering output against the cost of the engagement.
The velocity, measured honestly
Commits per engineer, per month
The team, not counting my own output. The lift came from one week of enablement and held all month, because they own the workflow. Normalized for headcount, so this is each person shipping more, not just more people.
avg before me ~52
I joined mid May
53
Jan
70
Feb
29
Mar
57
Apr
42
May
93
Jun
What the seven weeks actually were
the weeklong intensive

Made the team twice as fast, and left them owning it

One week working alongside them. Each engineer came out shipping about twice as fast, and it held all month, because I installed an AI workflow they run themselves. Idea to spec to review to ship, with automated senior review built in. Around 48 initiatives followed, most run by teammates.

ongoing · remediation

Cleared the performance ceiling

Instrument first, build a safety net, then fix. A core read went from 13 minutes to about 15 seconds. A common delete went from hundreds of thousands of database reads to a handful. The class of problem raw AI gets confidently wrong.

ongoing · the system

Built a library of reusable skills and agents

Continuous integration and deployment, async infrastructure, a test and safety harness, observability, and a set of skills and agents the team keeps using. The foundation every feature now ships on.

ongoing · the bigger ask

Then the part most teams don't know how to attempt: I built real AI into their product.

Not developers using AI to write code faster. AI built into the product itself. An assistant that can safely propose, preview, and undo real changes to live customer data, backed by full change history. Most companies this size can't attempt it, and most don't yet know where to start. This is the work I am finishing for JENGAI now, and it is the capability I most want to build for the next team.

The math the board actually cared about: value in, value out.

Seven weeks of senior work returned a lift equivalent to a couple of additional engineers of capacity, plus a platform and an AI system that keep paying off after I leave. The return is not a one week bump. It compounds, because the team owns what I built.

Velocity per engineer~2x
Deliverables shipped per month~2.7x
Annualized return on cost~2 to 3x
Who owns it nowthe team
In their words

JENGAI has real customers and a $2M round closing. The product works. What we needed was to level up how our team builds with AI without falling into the slop trap most teams hit at our stage. Min showed up and did exactly that.

Senior engineering judgment combined with actual AI fluency is the rarest hire of 2026. Mine is named Min.

— Shayne Paterson, CEO, JENGAI

Your team is already paying for the tools. Let's get the multiplier.

Tell me what you are shipping and what is getting in the way. The more specific, the more useful I can be on the first call.

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