Computer vision development for image and video AI: classification, detection, and visual inspection systems, engineered on the same data pipeline discipline as our production work.
A model that works on your training set can still fail in production
Computer vision models are unusually sensitive to the gap between training data and real-world conditions lighting, camera angle, image quality all shift after deployment. Teams that treat vision as a one-time model-training exercise, rather than an ongoing pipeline with monitoring, end up with accuracy that quietly degrades and nobody notices until it's a customer complaint.
What a vision engagement covers
- Image classification and object detection pipelines built for your actual data conditions
- Visual inspection and quality-control systems for operational workflows
- Data labeling and augmentation pipelines that reflect real deployment conditions
- Model serving infrastructure sized for your latency and volume requirements
- Drift monitoring so accuracy degradation gets caught, not discovered downstream
How we work
Audit the real images or video the system will see not a clean benchmark dataset
Build the data and labeling pipeline before optimizing the model
Validate accuracy against held-out real-world examples, not just training metrics
Instrument for drift so a model that degrades in the field gets flagged automatically
Typical stack
Frequently asked questions
It is building systems that extract information from images or video classifying what's in an image, detecting and locating objects, or flagging visual anomalies and integrating that output into a real workflow, not just training a model in a notebook.
Almost always because production conditions differ from training data different lighting, camera angles, image compression, or edge cases the training set never included. This is the single biggest reason vision projects underperform after launch, and it's a data pipeline problem more than a modeling one.
Less than most teams assume, if you use the right approach: pre-trained foundation models with targeted fine-tuning on a smaller labeled set often outperform training from scratch. We assess your actual data volume before recommending an approach, rather than defaulting to the most expensive option.