Every fab runs on knowledge. We make it last.
Ensign 14 builds AI copilots for semiconductor fab equipment. They watch live signals, recognize trouble early, and guide technicians to the right fix, using the knowledge your fab already has.
Deployed in a working semiconductor fab.
A fab is thousands of tools and a few people who truly understand them.
Every tool has a personality. A technician who has run it for years can sense trouble coming: a current that sits a little high, a pump that takes a moment longer. That judgment lives in people. It is not in the manual, and it does not survive a retirement or a job change.
When something does fail, the clock starts. A fault code points to a subsystem, not a cause. The manual runs to hundreds of pages. Teams replace parts that turn out to be fine, wait for the one person who has seen it before, and lose production while they do.
The data to do better already exists. Every tool streams it. Nobody has time to read all of it.
A copilot that reads everything, remembers everything, and shows its work.
Ensign 14 places a copilot beside each tool. It reads the signals the tool already produces, learns what normal looks like for that tool, and when something moves it tells the technician what is happening, what to check first, and why.
It never takes the controls. People stay in charge of every decision. They simply stop starting from zero.
Several kinds of intelligence, each used where it is strongest.
One large model is not the right tool for a fab. Numbers drift slowly, faults have exact codes, and an answer that sounds right but is wrong costs real money. So the copilot works in layers, and each layer hands the next something solid.
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1
Known faults, answered instantly
Fault codes map straight to their documented meaning and procedure. Where the answer is already written down, there is no guessing.
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2
Drift, caught early
Models learn how each tool normally behaves and flag quiet departures long before a fault code fires.
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3
Memory of every incident
Each fault is stored with what was seen, what was done, and what finally fixed it. The next occurrence starts with that history.
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4
Guidance written from sources
A language model turns the layers above into a ranked checklist, cites the manual section or past case behind every step, and says so when it is not sure.
A fault, from first sign to lasting fix.
An illustrative example. Step through it.
The problems it takes off the floor.
| In the fab today | With Ensign 14 |
|---|---|
| Tools stop without warning. | Trouble is caught as drift, before the tool stops. |
| Diagnosis takes as long as it takes to find the right person. | A ranked first step arrives with the alert. |
| Parts are swapped until the fault goes away. | Recommendations point to the likely cause, so parts are replaced for a reason. |
| Knowledge leaves with the people who hold it. | Every resolved fault joins a memory the whole team shares. |
| Each shift handles the same fault differently. | Every shift starts from the same guidance. |
| New technicians learn slowly, one fault at a time. | They learn from real incidents on real tools, with the reasoning shown. |
| The answer is somewhere in a manual nobody can search at 3 a.m. | Ask in plain language and get the cited page. |
| Slow process change goes unseen until it hurts output. | Gradual change is tracked per tool, against its own history. |
Built for the way a fab has to work.
Stays inside the fab
Models run on hardware in your facility. Telemetry, logs, and documents stay on your network, and no cloud service is needed to answer a question.
Learns from your tools only
What the copilot learns from your fab stays with your fab. It is never mixed into another customer's system.
Cites its sources
Every recommendation points to a fault code, a manual section, or a past incident. When it cannot, it says it does not know and hands the problem to a person.
People decide
The copilot advises and never controls equipment. Every action is a technician's call.
Start with one tool. Grow to the whole floor.
Ensign 14 is already running in a working fab, starting on a tool that matters, with its own signals, manuals, and history. Once a copilot earns trust there, what it has learned carries across similar tools on the line, and eventually the whole facility builds one shared memory of how it behaves.
It connects through the data paths a fab already has, so there is nothing to rip out.
The promo.
A short film on what Ensign 14 builds. It is private, and opens only with the password we shared with you.