PeakOps: The AI Video Analysis Pivot That Almost Made Sense

This time felt different.
Finally, a problem you could see, touch, and point to.

Not abstract like retention or engagement.
Real problems: health code violations, safety hazards, cleanliness issues, PPE compliance.

Things that restaurant operators actually lose sleep over.
Things that shut locations down, fail health inspections, and hurt brands.

This was it. We'd found the real problem.

The Tech Was Impressive

We built the whole stack:

• AWS Rekognition for PPE detection (gloves, hairnets, face masks)
• Object detection for safety violations (blocked exits, spills, equipment issues)
• OCR for signage compliance (handwashing signs, temperature logs)
• Frame extraction and analysis pipelines with FFmpeg
• Compliance scoring algorithms across 11 categories
• Beautiful dashboards with finding details and corrective action tracking

The demos were stunning.
Upload a kitchen walkthrough video, watch the AI identify every issue, see the compliance scores update in real-time.

People's eyes lit up during demos. This was magic. This was the future.

The Problem Nobody Told Me About

Then we asked operators to actually use it.

"Just record a quick video walkthrough of your kitchen and upload it."

Simple, right?

Wrong.

The Upload Friction Wall

Restaurant managers are the busiest people I've ever met.
They're juggling:

• Staff scheduling (and call-offs)
• Inventory and ordering
• Customer complaints
• Equipment breakdowns
• Food prep and quality control
• Cash handling and deposits
• Training new hires
• Corporate reports and compliance forms

And we wanted them to:
Pull out their phone, record a 2-minute video, upload it to our platform, wait for processing, then review the results.

We'd built a solution that required them to add more work to fix their workload problem.

The Pilot That Taught Me Everything

We got a pilot with a 5-location restaurant group.
Week 1: They were excited. Uploaded 3 videos.

Week 2: 1 video.

Week 3: Nothing.

I called the GM to check in.
"The tool is great," he said. "We're just slammed. I'll get to it next week."

Next week never came.

When Your Solution Increases the Problem

We promised to reduce inspection overhead.
Instead, we added steps:

We turned a 5-minute habit into a 20-minute project.
And wondered why adoption stalled.

The Demo-to-Reality Gap

Our demos worked because we controlled everything:
Pre-recorded videos, perfect lighting, cooperative environments, wifi that worked.

Reality was messier:

• Videos shot in poor lighting (kitchens aren't Instagram studios)
• Shaky footage from rushed walkthroughs
• Uploads failing on slow restaurant wifi
• 100MB+ video files eating mobile data
• Processing taking 3-5 minutes (felt like forever)
• AI missing obvious issues or flagging false positives

The tech worked in demos. It stumbled in the real world.

Building What I Found Elegant (Again)

I was so proud of the AI pipeline.
AWS Rekognition! Computer vision! Machine learning!

I built what impressed other engineers, not what helped restaurant managers.

A simple photo-based checklist would have been better.
Less impressive. More useful.

The Moment of Clarity

A franchise owner told me:

"I don't need a fancy system. I need my managers to actually check the fryers every morning. That's it."

That hit hard.
We'd built a sophisticated inspection platform when what they needed was behavior change.

AI couldn't create habits. Friction prevented adoption. Complexity killed consistency.

What I Learned

PeakOps was the closest we'd come to product-market fit.
But close isn't good enough when friction kills adoption.

We needed something lighter. Faster. Easier.
Something that fit into their day instead of adding to it.