The AI that watches your best worker, learns the job, and teaches the next one - so decades of know-how don't retire when the expert does.
On a manufacturing line, the most valuable manual is often the one that was never written. It lives in the hands of a veteran welder, a line lead, a maintenance tech who knows the exact sound a machine makes just before it fails. When that person retires, the knowledge tends to leave with them. DeepHow, a company founded in 2018 in Detroit, exists to stop that from happening.
The pitch is disarmingly practical. Point a camera at an expert doing the work. DeepHow's AI segments the footage into steps, transcribes and translates the narration, and structures it into a searchable how-to that any worker can pull up on a phone or tablet. What used to take weeks of shadowing becomes a training module produced, the company says, roughly ten times faster than conventional methods.
The founders would know the problem intimately. Sam Zheng, Wei-Liang Kao and Patrik Matos da Silva were innovation leaders at Siemens before starting the company. They watched enterprises spend heavily on machinery and comparatively little on transferring the human skill required to run it - a gap that widens every year as experienced workers age out of the workforce.
Their framing has stayed consistent since the beginning: this is AI to train workers, not to replace them. That distinction matters in an industry weary of automation promises, and it has helped DeepHow land inside some of the largest industrial names in the world.
Figures are company-reported and drawn from public announcements; treat performance metrics as approximate.
DeepHow began as a smart video-training platform. It has since pushed into what it calls physical AI - software that not only teaches a task but watches it happen and checks that it was done correctly. The product line now spans the full arc of operational knowledge: capture it, deliver it, and verify it on the line.
Records expert workflows and uses AI to segment, transcribe and structure them into searchable, multilingual step-by-step training.
Physical AI that confirms, in real time, whether an operator is following a standard operating procedure correctly.
On-demand vision AI that validates task execution and quality directly on the production line.
Automated classification of task steps to analyze cycle time and motion efficiency without a stopwatch.
AI-guided instructions and digitized work that coach frontline workers through complex tasks.
A compliance-oriented deployment tailored for pharma, biotech and medical-device manufacturers.
"It's even become a verb around here."
- Audrey Van de Castle, Stanley Black & Decker
DeepHow's customers are large industrial and manufacturing enterprises deploying across multiple plants and sites. The reported roster spans automotive, electronics, building products, consumer goods and heavily regulated pharma.
Manufacturing faces a demographic cliff: experienced workers are leaving faster than they can be replaced, and most of what they know was never documented. Onboarding a new hire has traditionally meant months of shadowing - expensive, slow, and impossible to scale across a global footprint.
DeepHow reframes that labor shortage as an information problem. If the expertise can be captured, structured and made instantly searchable, one veteran's knowledge can train thousands - in multiple languages, on demand, on the floor.
The connected-worker and frontline-training space is crowded - Augmentir, Poka, Tulip and a long tail of work-instruction and LMS tools all compete for the same plant budgets. DeepHow's wedge is automation of the hardest, most tedious step: turning raw footage of an expert into structured, teachable content without an instructional designer typing it all up.
Its second differentiator is verification. Where most tools stop at delivering instructions, DeepHow's physical-AI products aim to watch the task and confirm it was performed to standard. Capture, guide, check - that closed loop is what the company is betting the next phase of industrial AI will require.
DeepHow sells subscription access to its AI knowledge and verification platform, typically rolled out across a customer's plants and sites, with tailored deployments for regulated sectors. It sits at the intersection of enterprise AI, workforce training and industrial operations - a category increasingly labeled "connected worker," now sharpened by the shift toward physical AI that understands the real world well enough to coach a human through it.
Headquartered in Detroit with a team of roughly 61, the company has kept its focus deliberately unglamorous: the durable, hands-on work that many assumed was too physical to automate.
Lead investor: Sierra Ventures. Also backed by Osage Venture Partners, Qualcomm Ventures, Foothill Ventures, Techstars and Plug and Play.
Former Siemens innovation leaders launch DeepHow to solve industrial knowledge transfer.
The company goes through accelerator programs while building its AI video platform.
Sierra Ventures leads a round to bridge the skills gap in manufacturing, service and construction.
Closes Series A to scale enterprise knowledge capture and transfer.
Teams with University at Albany to train technicians for advanced chip manufacturing.
Expands from training into live SOP verification, visual inspection and time-and-motion analysis.
"We call DeepHow our USG YouTube."
Paul Bullock - USG
"DeepHow is the way we do business every day."
Eric Cotterman
"It's even become a verb around here."
Audrey Van de Castle - Stanley Black & Decker
Links open a YouTube search for the latest official videos.
Sources: company website and public funding announcements (PR Newswire, Techstars, University at Albany, Tracxn). Metrics are company-reported and approximate.