How much training data do you need?

Less than most people expect for classification, more than most expect for rare defects — you cannot learn a defect you have almost no examples of. We assess this before committing to an approach, and where examples are scarce we will say so and propose a rules-based or hybrid method rather than a model that looks confident and is not.

What happens when the system is not sure?

It stops and escalates. Every system we build has an explicit confidence threshold and a defined reject path, agreed with you during scoping. A vision system that guesses under uncertainty is worse than no vision system, because it removes the human who would have caught it.

Do the images leave our facility?

Not unless you want them to. Inference runs on edge hardware in your plant. Training data handling is agreed in writing, and on-premises training is available where images are sensitive.

Robotics /Technology & AI

Robotic Vision & Perception

Name: Coordinatez
Address: 8745 W. Higgins Road, Suite 110, Chicago, IL, 60631, US
Telephone: +1 (872) 258-2235

Vision is what separates a robot repeating a fixed path from one that handles the real world, where parts arrive rotated, overlapping, dusty, and occasionally wrong. A vision system has to answer three questions reliably: what is this, where exactly is it, and is it acceptable.

The third question is where most projects get uncomfortable. A model that is 97% accurate is also wrong roughly once every thirty-three parts, and what happens on that part matters more than the headline number. We build vision with the confidence threshold, the reject path, and the human escalation designed in from the start, and we measure accuracy against your parts and your lighting rather than a benchmark dataset that flatters everyone.

Discuss your project All technology services

The problem

Vision demos succeed on clean parts in good light. Production has glare, dust, overlap, and the variant nobody photographed — and a system with no defined behavior for uncertainty will confidently do the wrong thing.

How Coordinatez solves it

We build perception against your actual parts and lighting, define the confidence threshold and reject path explicitly, and ship an evaluation harness so accuracy is measured over time rather than asserted once.

What you gain

Accuracy measured on your parts, with the test set kept and re-runnable
Explicit low-confidence behavior — stop and escalate, never guess
Runs at line speed on edge hardware, with no cloud round-trip
A defined retraining path for when the part mix changes

Typical use cases

Bin picking and pose estimation for unstructured parts
Surface defect and finish inspection
Label, print, and barcode verification
Dimensional and tolerance checks
Assembly presence and absence verification

Technologies

OpenCV
PyTorch
Segmentation & detection models
2D and 3D cameras
Point-cloud processing
NVIDIA Jetson
TensorRT / ONNX

Engineering is delivered by Coordinatez's in-house team, serving clients across the United States and internationally.

FAQ

Robotic Vision & Perception — common questions

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Start a conversation

A build to scope, or a process to automate — tell us what you're working on.

Every inquiry reaches our engineering leads directly — and you'll hear back within one business day.

Talk to Coordinatez

support@coordinatez.com

+1 (872) 258-2235

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Robotic Vision & Perception