Defects moving too fast for human eyes. Anomalies in scan number 47 of the day. Patterns in imagery no analyst could process in a lifetime. We build computer vision systems that see what your team can’t.
Object detection. Segmentation. Classification. Edge deployment.
Build Your Vision SystemComputer vision isn’t one thing. It’s a spectrum from simple classification to pixel-perfect segmentation. The right system depends on the question your business needs answered.
Binary decisions at superhuman speed. One image in, one answer out. This is how production lines inspect 10,000 units per hour without hiring a second shift.
Find every object of interest, draw a box around it, count them, track them. Real time on video feeds. The backbone of surveillance, inventory, and safety systems.
Every single pixel gets classified. Not just “there is a tumor” but “these exact pixels are the tumor.” The hardest problem in computer vision. The one we hold the world record in.
Generate synthetic images for training, augment datasets 1,000x. When real data is limited or privacy-restricted, generation fills the gap.
One of the world’s most prestigious medical research institutions needed something that didn’t exist: fully automatic organ segmentation from CT and MRI scans. Commercial software required manual tracing, was painfully slow, and couldn’t segment vasculature at all.
We built the first fully automatic system. One click: organ isolated, vasculature mapped, 3D-printable file generated. Lungs, liver, whole brain, eight brain regions, bone, vasculature. All automatic.
The minimum accuracy across all organs: 98.41%. The maximum: 99.95%. Both numbers higher than anything in the published literature at the time of deployment.
It’s the Data.
Deep learning needs thousands of training images. Most companies don’t have them. That’s where most CV projects die.
One of the world’s largest consumer goods conglomerates needed computer vision for robotic automation across research operations. 100,000 employees. Fewer than 100 training images.
Using proprietary augmentation techniques, we expanded that dataset one thousand fold. The result: a production CV system that automated a critical research workflow.
If you’ve been told you don’t have enough data to start, that may not be true.
Read the full case study →A radiologist reviews 50 scans a day and sometimes misses what exhaustion hides. A vision system reviews every scan, every pixel. We built the world’s most accurate medical image segmentation system. We can build yours.
99.95% Jaccard accuracy in organ segmentation
A human inspector checks one product every 30 seconds and still misses 5%. A vision system checks every unit, catches 99.5%, and never takes a break. Quality control that scales with output, not headcount.
99.5% defect detection at full production speed
Farmers walk fields to find problems. Drones with computer vision scan thousands of acres in hours, spotting crop stress weeks before it’s visible. Early detection saves entire harvests.
Weeks of early warning vs. visual inspection
Vision systems monitor shelf inventory in real time, triggering restocking before customers walk away. Planogram compliance, misplaced products, foot traffic. All from cameras you already have.
Real-time shelf monitoring across every aisle
A security guard monitors 16 cameras and catches maybe 5% of incidents. Computer vision watches every frame of every camera simultaneously and alerts only when something actually matters. Not motion detection. Behavioral understanding.
100% coverage across all cameras, 24/7
Package dimension measurement, label reading, damage detection, loading optimization. Computer vision at the speed of your conveyor belt. Every package scanned, measured, and routed without human handling.
Structured data extraction from unstructured documents
Not every camera can wait for a server response. Assembly lines move fast. Drones lose signal. We deploy models directly on the hardware that needs them.
Models optimized for cameras, drones, and embedded devices. Millisecond inference, no cloud round-trip.
Process video at the edge. Send only results and alerts to the cloud. Cut bandwidth costs by 95% compared to streaming raw video.
No internet required. Critical for factories, remote sites, and security systems where connectivity isn’t guaranteed.
30+ FPS on production hardware. Fast enough for assembly lines, safety monitoring, and autonomous navigation.
When someone tells you their model is “99% accurate,” that may be true, but accurate on what data? Their test set or yours?
A model that’s 99% accurate on ImageNet might be 80% accurate on your factory floor. Different lighting, different angles, different defects. We measure accuracy on your images, your edge cases, your definition of failure.
Missing a defect costs you a recall. Flagging a good product costs you production time. These are different costs, and the right model depends on which one you can’t afford. We tune to match your actual cost function, not a generic benchmark.
Every model makes mistakes. The question is what happens when yours does. Confidence thresholds flag uncertain predictions. Human-in-the-loop catches edge cases. Automatic retraining prevents drift. Your model stays sharp.
What images do you have? How many? What’s labeled? We audit your visual data, define the problem, and design the architecture. If data is limited, we design the augmentation strategy.
We build the ingestion pipeline, set up annotation workflows, and create the training dataset. For projects with limited data, we run our proprietary augmentation pipeline. 100 images become 100,000. Quality controlled at every step.
Multiple architectures tested against your data. Every iteration measured against your accuracy requirements, not generic benchmarks.
Model gets packaged for your target environment: cloud API, edge device, mobile app, or embedded system. We handle the engineering: inference optimization, API design, monitoring, and alerting. Not a research prototype. A production system.
Lighting shifts, products update, new edge cases appear. We monitor drift, flag degradation, and retrain when needed. The model you launch is the worst version you’ll ever have.
If a human has to watch it, it doesn’t scale.
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