Regal Technology Partners, a Tier 2 defense electronics manufacturer supplying the industry's largest contractors, and nBrain, a 6-person advanced AI development team, launched AI in six months and integrated it across finance, the factory floor, and engineering and sales, in one of the most security-restricted industries there is. It worked because both sides went all in.
Plenty of companies have an AI pilot. Plenty of companies ten times Regal's size, with bigger budgets and bigger IT departments, still haven't put AI to work across their business. Regal has, and it is among the first in defense manufacturing to do it. In six months it went from a scope document to a private platform, RTP.OI, that runs invoices in finance, the morning build decisions on the factory floor, and the drawings and parts lists behind every quote.
nBrain designed it, built it, and runs it with Regal. Regal owns it: the platform, the data, and everything it learns.
Bills come out of the shared mailbox, get matched, and wait for a person to approve.
Which jobs have every part on hand and can start today, and what is blocking the rest.
Decodes customer drawings and parts lists into orderable parts, and supports the bid decision.
Ask a question about the operation in plain English. It only sees what each person's role is allowed to see.
Courses, certifications, and how the work is really done, so the next hire learns from the last expert.
Hours saved as the headline measure. Access set by role for every module.
The next modules on the same platform, drawing on the same data.
Status as of October 2026.
Customer drawings and parts lists that were once decoded line by line by hand now arrive at the quote already turned into orderable parts, for a person to check and sign.
A Tier 2 manufacturer and a small AI team, making moves most of the industry's largest players are still studying.
A Tier 2 manufacturer now has capability most companies its size don't. Work that lived in a few people's heads is on a platform the whole company can use and check. It is set up to move faster on quotes and build decisions. And it owns the system outright, instead of renting a tool its competitors can rent too.
It's proof that a small advanced AI development team can deliver what much larger firms haven't, in one of the hardest environments there is. And it's proof that Private, Owned AI works where public AI tools are off the table. If a Centralized AI Brain can run across a defense manufacturer, under export controls and strict security rules, with people signing every decision, it can work almost anywhere.
Defense manufacturers handle export-controlled technical data and controlled unclassified information. U.S. rules, including the export-control regulations known as ITAR and the Defense Department's cybersecurity standard for contractors, known as CMMC, limit where that data can live, who can see it, and which tools can touch it.
That puts public AI tools and off-the-shelf copilots effectively off the table. One wrong upload can be a compliance violation. It's a big reason much of the industry is still waiting. Regal and nBrain solved it together, as one team.
Regal's CEO, its IT lead, and its operations leadership owned the security rules and the decision rights. When a question came up about a kind of data, Regal made the call. On the drawing decoder, the hardest piece, the two teams worked through together which data counted as controlled and how each kind would be handled.
nBrain designed the platform around those rules from the first week, not as a layer added at the end. Security was part of the architecture, the access model, and every module's workflow before any of it went live. How Regal's data is secured →
Regal Technology Partners is a U.S. defense electronics manufacturer with about 120 people, a Tier 2 supplier that builds for the industry's largest contractors. It didn't set out to buy AI software or hand out chatbot seats. Clearing security was only the first hurdle. Most AI projects that clear it still go live, then quietly die. So Regal set out to choose an AI development partner: someone to build on Regal's own data, inside its own security boundary, around the way its people actually work.
Regal passed on the big firm for nBrain for one reason. nBrain kept asking about their employees. Who opens the invoice mailbox every morning? Who decides which jobs can run today, and what do they still do by hand?
The knowledge that ran the business didn't live in any system. It lived with a handful of people who had done the work for years, and in the places they kept it. Neither side could fix that alone: nBrain could build, but only Regal's people knew how the work was really done.
The two teams built it in order: start where the pain is loud and the data is clean, then earn the right to take on harder work. Every piece was designed with the person who does the job, at their desk.
Bills are pulled out of the shared mailbox a clerk used to work through by hand, matched, and queued. A person approves every one.
Each morning, supervisors see which jobs have every part on hand and can run today.
Reading customer drawings and parts lists line by line and turning them into the right, orderable parts.
A human stays in the loop, everywhere. The system surfaces the work, a person decides, a person acts, and the system learns. Automation is earned one step at a time, once people trust it.
This is where the partnership proved itself. nBrain's engineer could write the code. What he didn't have was the knowledge of how Regal actually decodes a drawing, and he said so.
"I decoded it wrong. Why? Because I'm human. When you're doing two hundred lines of decoding, how do you expect yourself not to make mistakes?"Maxime Hebras, Program Lead, Regal Technology Partners · September 18, 2026 · Pending client review
One wrong line on a drawing can mean the wrong part ordered and months lost waiting for a replacement. Four days after the builder said he was stuck, a system taught by Regal's own people caught exactly that kind of mistake.
The code was never the constraint.Understanding was.
Anyone can generate working code today. What no tool can do is know what to build, why the work is done the way it is, and where a wrong answer does real damage. That lives in people, on both sides of the table.
This is how a Tier 2 manufacturer and a small AI team did what much larger companies haven't. In August, after walking Regal's CEO through the platform line by line, Regal's VP of Operations sent the whole team a thank-you and closed with the line both sides had rallied around.
"Successful AI adoption is 50% vendor, 50% client. What we're proving is what the two halves look like when they're both ALL-IN."Danny DeMichele, nBrain, as quoted by Scott Lantiegne, VP of Operations, Regal Technology Partners · Email to the full team, August 21, 2026 · Pending client review
If either half is missing, the commitment fails. Usually quietly, months later, as a platform nobody logs into.
A Tier 2 manufacturer. A small AI team.Ahead of companies ten times their size.
It worked because both sides went all in. Regal calls its platform RTP.OI, for partnering intelligence, and that's what happened here: two teams moving what one person knows into something everyone can use and check. nBrain calls it a Centralized AI Brain: one private platform, owned by Regal, built on Regal's own data, and used across the business.
Most AI talks are theory or product demos. This is an implementation story from one of the most security-restricted industries there is, told by both sides of the table: a Tier 2 defense manufacturer and the advanced AI development team that built with them.
Export controls and strict security rules keep most defense manufacturers away from AI. One mid-market manufacturer and its AI development partner built a private platform inside those rules and made it stick in six months: invoices first, then the factory floor, then the hardest problem in the building. The session opens with the 2:28 film, explains how the security problem was solved at the principle level, walks through the week the builder said "I'm stuck," and leaves the audience with the 50/50 model and five commitments they can test their own organization against.
Quotes are from recorded calls and emails, March–September 2026. Customer, program, and part-number details have been removed.
In one strategy session we map where a Brain would return the most time, what we'd build first, and how we'd measure it from month one.
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