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 delivery system they own. It kept compounding after that. By July, eight engineers were shipping 537 deliverables a month against 60 in May, and my own commit count had fallen to roughly zero.
What began as Weeklong Agentic Workflows Training became an ongoing engagement. The week proved it, then Shayne kept me on to clear the performance ceiling and build real AI into the product itself.
One week working alongside them. Each engineer came out shipping about twice as fast, and it held, because I installed a delivery system they run themselves. Idea to spec to review to ship, with automated senior review built in before a human ever looks.
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.
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.
This is the number that matters more than the velocity. If a team ships five times more and breaks five times more, nothing was gained. From May to July, feature work grew 20x while defect work grew 3.9x. More capacity went into new product, not into repair.
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 cannot attempt it, and most do not 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 lift was not a one week bump. It compounded for three months after the install week, and it kept compounding once I stopped writing code, because the team owns the system rather than renting it from me.
Found it, closed it, and moved every secret into a managed vault.
Traced one the team had been chasing the wrong way, then made that whole failure class unrepeatable.
Most people selling AI process have never carried the consequences of a slow engineering organization at scale, or operated where "move faster" was a board mandate rather than a slogan. I have, on both sides of the table.
Ship fast and do not break production have to coexist. The same tension your org lives in daily, without a green field to escape to.
Not an advisor. The executive accountable for the technology, through a real exit, and judged on the outcome.
Shipping under a quality bar set by millions of concurrent players who notice the moment something ships sloppy. Senior judgment as a shipping requirement, not a phrase.
The discipline built around this problem, at the scale of the largest engineering org on earth: how thousands of engineers ship faster without shipping worse.
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.
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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