Attended NAMES 2026 this week. One surprising takeaway, it wasn't AI. Everyone was talking about that. It was the focus on Human Operational Excellence throughout the sessions. Kevin Voelkel SVP of Manufacturing at Toyota North America, even mentioned that frontline employees were the #1 priority. Thank you to everyone who shared their perspectives and took the time to connect with us. We appreciated the time spent connecting with peers across the industry.
DeepHow
Software Development
Detroit, Michigan 12,589 followers
Physical AI for Operational Excellence
About us
We standardize, verify, and improve frontline performance across manufacturing, pharma, and utilities. DeepHow delivers Operational Excellence through Physical AI, unifying knowledge and verification into a single platform that drives compliance, quality, and productivity where work happens: on the floor, in the field, and at the point of execution. What We Do: Knowledge Capture & Transfer (KCT) Capture explicit and tacit know-how 90% faster than traditional methods. Reduce onboarding time by up to 77% and create a scalable operation that preserves expertise across sites. Operational Output Verification (OOV) Verify work outputs out of the box with accuracy. Cross-reference photo and video evidence of work outputs against SOPs and documentation to ensure right-first-time execution and drive measurable productivity gains. Live SOP Verification (LSV) Detect non-conformant steps in real time with up to 96% accuracy. Validate operator steps as they happen and highlight deviations immediately, improving first-pass yield and preventing deviations before they affect quality. Time and Motion AI (TMA) Balance lines and complete time & motion analysis faster than current semi-automated approaches. Automatically classify operator activity, eliminate waste, and drive continuous improvement in lean manufacturing environments.
- Website
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https://www.deephow.com/
External link for DeepHow
- Industry
- Software Development
- Company size
- 51-200 employees
- Headquarters
- Detroit, Michigan
- Type
- Privately Held
- Founded
- 2018
- Specialties
- Manufacturing, Skills Tech, Training Software, Industrial, AI, Workforce Development, Workforce Readiness, Smart Training Videos, Frontline Operations, Frontline, Shop Floor, Manufacturing Operations, Operations Software, Upskilling, Cross-Skilling, Physical AI, AI Verification, SOP Verification, Time & Motion Intelligence, and Pharma & Life Sciences
Locations
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Primary
Get directions
Detroit, Michigan 48226, US
Employees at DeepHow
Updates
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Most manufacturers have solved the knowledge capture problem. The harder problem? Knowing if that knowledge is actually being executed correctly, every time, on every shift. At DeepHow, we started where the industry started: capturing expert knowledge before it retired with your workforce. Video-based, step-by-step, searchable on the floor. It worked. But it wasn't enough. So we built 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐀𝐈 that closes the loop: 𝐕𝐢𝐬𝐢𝐨𝐧 𝐀𝐈 verifies task execution in real time flagging deviations before they become defects. 𝐓𝐢𝐦𝐞 & 𝐌𝐨𝐭𝐢𝐨𝐧 𝐀𝐈 turns every shift into performance data no manual studies, no one-time snapshots. 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐎𝐮𝐭𝐩𝐮𝐭 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 validated the result, not just the process. 𝐋𝐢𝐯𝐞 𝐒𝐎𝐏 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 validates each step the operator takes, guides them, and reinforces the SOP without interrupting their workflow. The entry point looks different for every manufacturer. The destination is the same: operations where knowledge, execution, and performance work as one. We started by capturing knowledge. Now we make sure it's being used 𝐜𝐨𝐫𝐫𝐞𝐜𝐭𝐥𝐲, 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭𝐥𝐲, 𝐦𝐞𝐚𝐬𝐮𝐫𝐚𝐛𝐥𝐲. 𝐑𝐞𝐚𝐝 𝐦𝐨𝐫𝐞: https://lnkd.in/g8V-W9iE #PhysicalAI #VisionAI #Manufacturing #DeepHow #OperationalExcellence #LeanManufacturing #FrontlineAI
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𝐓𝐡𝐞 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐅𝐥𝐨𝐨𝐫 𝐇𝐚𝐬 𝐄𝐲𝐞𝐬. 𝐀𝐧𝐝 𝐈𝐭'𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠. Next week, the DeepHow team will be at 𝐍𝐀𝐌𝐄𝐒26 and one topic is already dominating every conversation: how do we get people performing at their best, faster, and more consistently at scale? The answer is Physical AI and Vision AI. And the industry is moving fast. The global Physical AI market is projected to grow from $81B in 2025 to nearly $1 trillion by 2033. Vision AI is now the #1 emerging priority in manufacturing automation, outpacing large language models and humanoid robotics in immediate factory-floor adoption. The most exciting frontier is not just machine monitoring. It is human performance. AI that guides operators through complex procedures step by step. AI that verifies the right steps were completed correctly before work moves on. AI that coaches improvement over time, not just flags problems after the fact. This is exactly what we are solving at DeepHow. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐂𝐚𝐩𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 turns expert knowledge into AI-powered SOPs, accessible to anyone on the floor. 𝐋𝐢𝐯𝐞 𝐒𝐎𝐏 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 uses Vision AI to verify steps in real time, flag deviations as they occur, and guide operators back on track without interrupting the flow. 𝐓𝐢𝐦𝐞 𝐚𝐧𝐝 𝐌𝐨𝐭𝐢𝐨𝐧 𝐀𝐈 automatically analyzes cycle times, motion patterns, and efficiency opportunities, giving teams the data to balance lines against takt time. If you are heading to NAMES26, we would love to connect. Drop a comment, stop by 𝐁𝐨𝐨𝐭𝐡 '22' and connect with Mark Schwans and Evan McGrath. #NAMES26 #PhysicalAI #VisionAI #Manufacturing #DeepHow #SmartFactory #KnowledgeTransfer #DigitalTransformation #OperationalExcellence
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Most manufacturers know what the right process looks like. The hard part is knowing if it's actually happening on every shift, at every station, by every operator. That's exactly what we're bringing to NAMES 2026. Find DeepHow at NAMES'26 to showcase how Physical AI and Vision AI are transforming frontline execution: → Capture how your top performers work and replicate it. → Verify task execution the moment it happens, not after the fact. → Run automated Time & Motion studies to find where performance is lost. Booth 22 | Houston, TX | April 20–22 Find Evan McGrath and Mark Schwans — let's talk execution. #NAMES2026 #PhysicalAI #VisionAI #Manufacturing #DeepHow
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Capture Know-How. Train People and AI. Verify and Optimize Actions. See How... #DeepHow #FutureOfWork #DigitalTransformation
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We'll be at NAMES'2026 (April 20–22) and we're bringing something worth seeing a closer look at how manufacturers are finally connecting knowing the right way to do something, verifying they did it the right way, and then optimizing their actions and productivity. How? Knowledge Capture and Transfer: Capture your experts knowledge and best practices, and ensure that they are transferred to everyone who needs them. Operation Output Verification: Know that what you produced was correct. Multi point inspection and validation against standards. Live SOP Verification: Ensure that every step the operator takes is the right one. Correct deviations and mistakes before they become downstream issues. Time and Motion AI: Automate time and motion studies. Using Physical AI, classify tasks, remove MUDA and optimize your lines against takt time. Come find us at Booth [22]. Engage with Evan McGrath and Mark Schwans. Let’s talk about what can now be done in manufacturing today! #deephow #NAMES2026
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Static SOPs slow teams down. Smart Videos level them up. DeepHow turns expert workflows into step‑by‑step visual SOPs auto‑structured, multilingual, and easy to follow anywhere. Learn more at: https://lnkd.in/gaK5cxXV #AI #SOPs #DeepHow
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Hear Audrey Van de Castle from Stanley Black & Decker, Inc. and Allison Kuhn from LNS Research on why AI is becoming essential in preserving expertise and boosting technical skills across manufacturing and the trades. As up to 90% of manufacturers struggle with knowledge loss and workforce turnover, AI Powered Knowledge Management is helping fill the gaps faster and more efficiently. Watch the full webinar: https://lnkd.in/dFSMY9jF #DeepHow
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What if quality could be verified in real time? With DeepHow’s AI-powered SOP monitoring, every step becomes visible, guided, and error‑free. Observe → Verify → Improve. Learn more: https://lnkd.in/giGRTqFy #DeepHow #SOP #OperationsExcellence #QualityManagement
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Our CEO always asks us to question before jumping to any answer. Here again is a perfect example of a question for us all. Why do we have SOPs written the way they are? Now layer in AI, and a workforce that consumes information completely differently… and you have to ask it again. So now what?
SOPs Were Written for Humans. Humans Stopped Reading Them. And AI came along, Now What? There is a fascinating conversation happening in the software world right now. For decades, developers wrote documentation for other humans to read. Clean structure, clear headings, context for every function, detailed explanations of intent. Good documentation was a mark of a good engineer. Then something shifted. The conversation today is around the fact that the primary consumer of code documentation is no longer a human. It is an AI model. You take your pick. The way you write documentation, the structure you use, the level of detail you include, all of that is being re-examined because the audience has fundamentally changed. I think the exact same conversation needs to happen for SOPs. One thing most experts agree on: people do not actually read the SOPs. Not the way they were intended to be read, anyway. They are reference documents. You pull them up (maybe) when something goes wrong, or when you are onboarding someone new, or when an auditor walks in. A frontline operator reading a 40-page document was, anyway, fiction. So we have a document that was designed to be read linearly by humans, but humans were not reading linearly anyway. And now we are heading into a world where the next generation of workers is even less likely to engage with documents that way. Which raises the real question: who are these documents actually for? If the answer is compliance, fine. That is a legitimate answer. Regulatory frameworks move slowly; that world will catch up eventually. But a significant chunk of SOPs and job instructions are not primarily for compliance. They exist to transfer knowledge and to ensure the right person does the right thing in the right sequence - as a reference. That is a fundamentally different purpose, and it can be served very differently from a formatted PDF document with a revision history table. What if the right format for that knowledge is not a document at all? What if it is structured data, video, captured work patterns, indexed into a system where AI can retrieve and synthesize it on demand, in context, for the specific person asking the specific question at that specific moment? Is this not exactly what we are already seeing happen? We seem to have seamlessly stepped into the world of "Dynamic Knowledge Retrieval," and the knowledge creation has to catch up. The audio-book analogy I keep coming back to is this: if you buy an audiobook, transcribe it, and then print it out to read, you have completely missed the point of the audiobook. A lot of what is happening today is that transition moment, where the instinct is to layer new capability onto a familiar structure. Change is hard, and existing systems are real. How do we think about this problem? Should the question be "how do we digitize our SOPs," or "Why are we still organizing knowledge this way at all?" That is the conversation worth having.