Every pack photographed. Every docket searchable. Every anomaly surfaced — without a single frame leaving the warehouse.
Havit's packing line runs 16 stations, each producing dockets that get taped to outgoing cartons. When a customer disputed an order, the QC team had two options: trust the paper trail, or spend hours reviewing raw CCTV.
Neither scaled. The existing cameras were only useful after the fact — there was no way to search by docket number, no confidence that anything had actually been captured, and no record tying a pack to the product leaving the door.
We proposed a local-AI vision system that monitors all 16 stations continuously, captures the best frame of every pack, reads the docket number with OCR, and makes the whole thing searchable in seconds.
Every part runs on hardware in the warehouse. No cloud APIs, no monthly per-seat fees, no data leaving the premises. The system was designed so the QC team could use it immediately and the engineering team could extend it without us.
Classic computer vision watches each station in real time and fires only when a docket appears or leaves the packing zone. At that moment — and only then — the local AI model runs on the captured frames. The result is continuous monitoring on modest hardware.
Commissioning tool and ongoing health monitor. Built for a warehouse manager who needs to see, at a glance, whether all 16 stations are healthy.
The daily tool. Type a docket number, get the full photo record in under five seconds. No training, no manual, no friction.
The core system — everything needed to go live and be useful from day one.
Make the system smarter using real operational data.
Connect the QC record to the broader business — at the customer's request.
"Every pack is accounted for — and we can pull the evidence in five seconds instead of half a day."
If there's a workflow you'd quietly kill for — hours saved, photos you can't find, manual steps that never scale — we'd like to hear about it. Initial conversations are free.
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