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
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Inside the episode…
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A logistics company's urgent copper-measurement problem with no clear owner
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The hidden cost and inefficiency of manually measuring more than 5,000 pallets
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How a single office hours conversation reframed the problem as a vision-AI opportunity
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Training a custom vision model using pallet photos and simple index cards
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Rapid prototyping with automation and vision tooling to ship in days
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Over $150K in cost savings and a dramatically better experience for warehouse teams
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Why involving frontline workers accelerated adoption and improved feedback loops
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Letting go of perfection and embracing statistically "good enough" outcomes
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What this teaches us about speed, trust, and momentum through small wins
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Where this approach goes next and why similar teams should be paying attention
Mentioned in this episode
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n8n (automation platform)
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Roboflow (vision model training)
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ChatGPT (image and text analysis)
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OpenAI API
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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.