The most accurate vision intelligence for the factory floor.

General models fail on the factory floor

Most vision systems are built on detectors trained on clean web images. A real factory floor is the opposite of clean: dust and glare, night-mode infrared, motion blur, oblique camera angles, crowded frames, and the one event that matters hidden in a corner at 2 a.m.

We train for those conditions specifically — and for the events that actually matter on a floor, not for a web leaderboard.

• Real CCTV conditions: low light and IR night mode, dust, blur, oblique angles
• Factory-specific understanding: fire and smoke, PPE, machines, forklifts, people
• Tuned for the rare event that matters, not the average web image

Our own models, our own data

Foreman runs on its own models, built and trained in-house. We build our own training datasets — purpose-collected and curated for factory environments — because that is the only way to be the most accurate at this one thing.

And we never train on our customers' footage. Your video stays yours; the data that improves our models is data we own and curate, not the cameras we are trusted to watch.

• Models built and trained in-house, for factory floors
• Datasets curated for real factory conditions, not generic imagery
• We never train on customer footage — your data stays yours

Measured, not just claimed

Accuracy claims are easy to make and hard to prove, so we prove ours. We evaluate our models against the published state of the art on factory-relevant tasks — on both accuracy and the on-device latency that decides whether an alert arrives while it still matters.

We are publishing the benchmark and a paper so the numbers are open to scrutiny, not just asserted.

• Evaluated on accuracy and real-time on-device latency together
• Measured against published baselines, not internal cherry-picks
• Benchmark and paper: coming soon

Two checks before you are alerted

Detection runs on-site, every camera, every second. Each possible event then gets a second, more thorough confirmation before it ever becomes an alert. That is what keeps alerts rare and real — and gives you clear photo proof when one fires.

Where this is headed

Today Foreman watches for what matters most on a floor: fire, intrusion, PPE, idle machines, headcount. That is the beginning.

The models and datasets we are building now are the foundation for something larger — a video search engine for the physical world, where you ask your cameras a question in plain language and get an answer. Best-in-class detection is how we earn the right to build it.