AI can now see the physical world well enough to work in it: read a date code at line speed, catch a low fill, flag a missing seal, notice the person who walked into the allergen zone without a hairnet. Geigyr designs and deploys these vision systems on your line — cameras, models, and the integration into how your plant actually runs — and keeps them accurate after the first month.
Vision projects fail when the demo was someone else's factory. Send us footage of your line — a phone video is enough to start — and we build a short demonstration of detection running on your product, your labels, your defects. You see what the system sees before you spend real money. No slideware, no stock footage.
Every package leaving the line carries the right label, the right lot, and a readable date code. The wrong-film-on-the-wrapper recall and the smeared code that gets a case rejected at the DC — caught before the pallet is wrapped.
Underfills that turn into customer complaints, overfills that quietly give away margin, and the seal contamination that becomes a leaker three weeks into shelf life. Checked on every unit, not every hundredth.
The glove tip, the shard, the piece of belt that metal detection and x-ray were never going to catch. A camera watching the open product zone adds a layer where your current controls have a known gap.
Crushed corners, open flaps, skewed labels, dented cans. The defects your customers photograph and deduct for — flagged at the case packer instead of at their receiving dock.
Hairnets, beard nets, smocks, and gloves in the zones that require them. Continuous, even-handed, and documented — instead of depending on whichever supervisor happens to be looking.
Photographic proof the line was clear before the changeover — no stray product, no leftover packaging from the previous run. The allergen-changeover record your auditor asks about, generated instead of initialed.
We walk the line and deal with the physical facts first: lighting, glare, camera angles, line speed, washdown requirements, where a camera can physically live. Getting this wrong is why most vision projects stall at the pilot.
A plan for capturing images of your product — good, bad, and borderline — from the cameras that will actually run the system. The model is only as honest as the examples it learned from.
Annotation, training, and testing against acceptance criteria we agree on up front: what must be caught, what false-reject rate the line can tolerate. You see the numbers, not a demo reel.
The system runs where it makes sense — on an edge device at the line for real-time decisions, or in the cloud where latency doesn't matter. Industrial hardware, specified by us, owned by you.
A detection that nobody acts on is a screensaver. We wire results into your world: reject gates and stack lights through the PLC, alerts to the floor lead, records into your QMS, events into the same corrective-action workflows that run your monitoring.
We prove the acceptance criteria on the live line, then stay on. Products change, packaging changes, seasons change the light through the windows. Keeping the model accurate over time is part of the service, not an upsell.
You license the vision platform directly from the vendor and buy the cameras and compute on our bill of materials. No markup on hardware, no reselling, no lock-in to us. We are paid for making it work.
We build on leading commercial computer vision platforms rather than research code — which means the model layer moves fast and keeps improving. What you are buying from Geigyr is everything the platform cannot do: the site assessment, the deployment, the integration, and the accountability.
The right first project is one check on one line with clear acceptance criteria — not a plant-wide vision program. Prove it, then expand to the next check with the same infrastructure.
Camera placement, lens choice, the glare that only shows up at 3 p.m. — vision systems live or die on physical details you cannot fix remotely. We are local to NJ, NY, and CT, and we come back when something changes.
Thirteen years in industrial data platforms before computer vision. PLCs, MQTT, historians, quality systems, alerting chains — the plumbing a detection has to reach before it is worth anything.
Every vision model drifts as products and conditions change. The retraining loop — new examples, updated model, re-verified accuracy — is built into the engagement from day one.
Vision flags; your team decides. Detections route to operators and quality staff with the image attached, and anything that touches your compliance records goes through the same rule-based documentation chain as our monitoring systems.
Tell us what you wish you could catch. If a camera can see it, we'll show you — on your own footage — before you commit to anything.
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