Capturing Knowledge was only half the battle.

When DeepHow started, the problem felt obvious. Skilled workers were retiring. Their knowledge was leaving with them. And nobody had a good answer for how to stop it.

So we built one. Capture the expert on video. Break it into steps. Make it searchable, translatable, and accessible on any device on the floor.

𝘚𝘪𝘮𝘱𝘭𝘦 𝘪𝘥𝘦𝘢. 𝘗𝘰𝘸𝘦𝘳𝘧𝘶𝘭 𝘰𝘶𝘵𝘤𝘰𝘮𝘦.

That was operational knowledge management. And it worked. Thousands of operators across hundreds of sites are learning faster, more effectively, and making fewer mistakes as a result.

But here's what we learned as we got deeper into manufacturing environments.

𝐓𝐡𝐞 𝐯𝐚𝐥𝐮𝐞 𝐢𝐬𝐧'𝐭 𝐰𝐡𝐞𝐭𝐡𝐞𝐫 𝐲𝐨𝐮𝐫 𝐨𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬 𝐰𝐚𝐭𝐜𝐡𝐞𝐝 𝐭𝐡𝐞 𝐯𝐢𝐝𝐞𝐨. 𝐈𝐭'𝐬 𝐰𝐡𝐞𝐭𝐡𝐞𝐫 𝐭𝐡𝐞𝐲 𝐞𝐱𝐞𝐜𝐮𝐭𝐞𝐝 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞 𝐜𝐨𝐫𝐫𝐞𝐜𝐭𝐥𝐲.

That's a fundamentally different problem.

𝘚𝘰 𝘸𝘦 𝘣𝘶𝘪𝘭𝘵 𝘷𝘦𝘳𝘪𝘧𝘪𝘤𝘢𝘵𝘪𝘰𝘯.

Vision AI that watches work as it's being performed, compares it against your approved standard, and flags deviations before they become defects, rework, or failed audits. Not after. Before. All in real time.

𝘉𝘶𝘵 𝘸𝘦 𝘥𝘪𝘥𝘯'𝘵 𝘴𝘵𝘰𝘱 𝘵𝘩𝘦𝘳𝘦.

Once you can verify how work is being done, a natural question follows: how long does it take? Where is time being lost between steps, between operators, between shifts?

Time-and-motion studies are nothing new. But they've always been expensive, infrequent, and manual. We've changed that. Every verified task is now a data point. Automatically classify productive vs. wasteful activity. Build Yamazumi-style charts. Balance the line, not as a one-time study, but continuously.

Every manufacturer has a different starting point.

Lean manufacturers often start with 𝐓𝐢𝐦𝐞 𝐚𝐧𝐝 𝐌𝐨𝐭𝐢𝐨𝐧 𝐀𝐈 for immediate cost savings and it fits into a well defined process that they can optimize. Others have hidden quality problems driving scrap and rework they can't fully explain. 𝐋𝐢𝐯𝐞 𝐒𝐎𝐏 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 gives them step-level visibility without interrupting workflow. Others need to validate the output of their work, not necessarily every step, and 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐎𝐮𝐭𝐩𝐮𝐭 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 is for them. And for organizations facing knowledge loss or a retiring workforce, 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐂𝐚𝐩𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 is the choice.

The entry point is different. The destination is the same.

That's the promise of Physical AI. Not replacing the operator. Just making sure that knowledge, execution, and performance are working together, so every batch, every build, and every shift benefits from the best your organization knows how to do.

We started by capturing knowledge. Now we make sure it's being used correctly, consistently, and measurably.