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In this episode, I walk through a real, high-stakes moment inside a warehousing and logistics operation, thousands of pallets of telecom cable, a hard year-end deadline, and a task nobody actually owned. The team was facing a potential six-figure hit, measuring precious metal content by hand with clipboards and micrometers, under serious time pressure. During a simple office hours session, we paused, reframed the problem, and realized this wasn't a labor issue at all. It was a vision problem.
What followed was a fast, scrappy sprint. Sales, warehouse staff, and engineers worked side by side. We prototyped, tested, and shipped a vision-based AI system in days, not months. Using lightweight tooling, we cut monthly costs by more than $150K, improved measurement accuracy, and delivered a working solution in under 72 hours. If you're skeptical about what "good enough" AI can actually do in the real world, this story is a clean proof point
Visit convergence.fm and contact us for to schedule your own office hours and get clarity and confidence tackling your toughest product and engineering challenges.
Inside the episode…
A logistics company's urgent copper-measurement problem with no clear owner
The hidden cost and inefficiency of manually measuring more than 5,000 pallets
How a single office hours conversation reframed the problem as a vision-AI opportunity
Training a custom vision model using pallet photos and simple index cards
Rapid prototyping with automation and vision tooling to ship in days
Over $150K in cost savings and a dramatically better experience for warehouse teams
Why involving frontline workers accelerated adoption and improved feedback loops
Letting go of perfection and embracing statistically "good enough" outcomes
What this teaches us about speed, trust, and momentum through small wins
Where this approach goes next and why similar teams should be paying attention
Mentioned in this episode
n8n (automation platform)
Roboflow (vision model training)
ChatGPT (image and text analysis)
OpenAI API
Subscribe to the Convergence podcast wherever you listen, and catch video episodes on YouTube at youtube.com/@convergencefmpodcast.
If this was useful, leave a five-star review and like the show on YouTube. That's how we grow.
Note: Visuals in the video form of this episode were generated by AI (Gemini) as the originals are sensitive and confidential to our customer and their staff.
By Ashok Sivanand4.9
1919 ratings
In this episode, I walk through a real, high-stakes moment inside a warehousing and logistics operation, thousands of pallets of telecom cable, a hard year-end deadline, and a task nobody actually owned. The team was facing a potential six-figure hit, measuring precious metal content by hand with clipboards and micrometers, under serious time pressure. During a simple office hours session, we paused, reframed the problem, and realized this wasn't a labor issue at all. It was a vision problem.
What followed was a fast, scrappy sprint. Sales, warehouse staff, and engineers worked side by side. We prototyped, tested, and shipped a vision-based AI system in days, not months. Using lightweight tooling, we cut monthly costs by more than $150K, improved measurement accuracy, and delivered a working solution in under 72 hours. If you're skeptical about what "good enough" AI can actually do in the real world, this story is a clean proof point
Visit convergence.fm and contact us for to schedule your own office hours and get clarity and confidence tackling your toughest product and engineering challenges.
Inside the episode…
A logistics company's urgent copper-measurement problem with no clear owner
The hidden cost and inefficiency of manually measuring more than 5,000 pallets
How a single office hours conversation reframed the problem as a vision-AI opportunity
Training a custom vision model using pallet photos and simple index cards
Rapid prototyping with automation and vision tooling to ship in days
Over $150K in cost savings and a dramatically better experience for warehouse teams
Why involving frontline workers accelerated adoption and improved feedback loops
Letting go of perfection and embracing statistically "good enough" outcomes
What this teaches us about speed, trust, and momentum through small wins
Where this approach goes next and why similar teams should be paying attention
Mentioned in this episode
n8n (automation platform)
Roboflow (vision model training)
ChatGPT (image and text analysis)
OpenAI API
Subscribe to the Convergence podcast wherever you listen, and catch video episodes on YouTube at youtube.com/@convergencefmpodcast.
If this was useful, leave a five-star review and like the show on YouTube. That's how we grow.
Note: Visuals in the video form of this episode were generated by AI (Gemini) as the originals are sensitive and confidential to our customer and their staff.

10,222 Listeners