ComputerVisionSolutions
Computer vision models that perform well on curated training data often fail on a dim, dusty factory floor or a cluttered store shelf. We build vision systems tested against your actual operating conditions, engineered to handle the mess of the real world.
Avg. defect detection accuracy
Avg. false positive reduction
Vision systems deployed
The gap between a computer vision demo and a computer vision system that survives a real factory floor or warehouse is almost entirely about data and edge cases, not model architecture. Lighting changes, camera angles drift, and objects get partially obscured in ways a clean benchmark dataset never anticipates. We build for that reality from day one.
Every engagement starts with a serious look at the actual physical environment: camera placement, lighting variability, and the specific defect or object type that matters. Forge Manufacturing's predictive maintenance work, for instance, depends on vision models that catch subtle wear patterns under inconsistent factory lighting, which required a very different training approach than a studio-lit product photo dataset.
We're pragmatic about build versus buy. Off-the-shelf vision APIs handle general object detection well; the cases that justify a custom model are usually specific defect types, specialized equipment, or accuracy requirements that generic models can't hit. We help clients make that call honestly rather than defaulting to a custom build every time.
Deployment matters as much as model accuracy. Some use cases need edge inference on-device for latency or connectivity reasons, like a warehouse camera with unreliable network access, while others run fine through a cloud API. We architect for whichever your physical environment actually demands.
What'sincluded
Defect & Quality Inspection Models
Custom vision models trained to catch specific defect types on a production line, tuned to your real lighting and camera setup.
Retail Shelf & Inventory Monitoring
Vision systems that track shelf stock, product placement, and planogram compliance from in-store camera feeds.
Logistics & Warehouse Vision
Package identification, damage detection, and volume estimation systems built for warehouse and dock environments.
Edge Deployment Engineering
On-device inference for environments with limited connectivity or strict latency requirements, optimized for real hardware constraints.
Model Retraining Pipelines
Ongoing retraining pipelines that adapt to new product lines, camera hardware, or seasonal changes in the physical environment.
Engagementprocess
Environment & Data Audit
We assess camera placement, lighting conditions, and available training data against the specific detection task you need solved.
Build vs. Buy Assessment
We evaluate whether an off-the-shelf vision API meets accuracy requirements or whether a custom model is genuinely justified.
Model Development
We build and train the model against real environment data, including edge cases like poor lighting and partial occlusion.
Field Testing
We test on-site under real operating conditions, not just held-out lab data, and tune based on actual false positive and negative rates.
Deployment & Monitoring
We deploy to edge or cloud as required and set up ongoing accuracy monitoring and retraining triggers.
Toolswereachfor
Relatedcasestudies
Industriesweapplythisin
Commonquestions
We train and test the model on data collected under your actual lighting conditions, including the worst realistic cases, rather than clean studio images. Where lighting variability is severe, we sometimes recommend physical fixes, like added fixed lighting, alongside the model work itself.