What accuracy can we expect from a custom computer vision model?
It depends on the task and the quality of your images. Industrial defect inspection typically lands in the high 90s once the data and lighting are controlled, while medical imaging applications often match or exceed human reviewers on the specific finding they are trained for. We do not promise a number up front. We build a proof of concept on your real images and report measured accuracy, precision, and recall before any full build.
How long does it take to build and deploy a computer vision system?
A focused proof of concept usually takes about 3 to 6 weeks, and a production deployment runs roughly 8 to 16 weeks depending on hardware integration and the number of conditions the model must handle. We give a firm timeline after the requirements and data assessment, since clean labeled images shorten the schedule and messy or sparse data lengthens it.
What images and data do you need to get started?
Representative images covering the conditions the system will face in production, including good cases, defect or positive cases, and the awkward edge cases. The more labeled examples of the thing you want detected, the better. If you have few labels or none, we handle the labeling, augmentation, and dataset construction as part of the build, the same way we do for our broader custom ML systems work.
Will it work with our existing cameras and equipment?
In most cases, yes. We integrate with standard industrial and security cameras and imaging systems, and we also work with thermal cameras, hyperspectral sensors, and medical imaging devices. We assess your current setup first and tell you honestly whether the existing hardware is good enough or whether a sensor or lighting change will materially improve accuracy.
How do you handle edge deployment and real-time speed?
We optimize models for the target hardware using pruning, quantization, and knowledge distillation, then deploy on devices like NVIDIA Jetson, Intel Movidius, or your own servers for low-latency inference. For high-throughput pipelines that score frames live and trigger actions, we pair this with our real-time inference engines.
How do you deal with changing lighting and image quality?
We build robust preprocessing and train with heavy data augmentation so the model tolerates varying light, shadows, angles, and noise. Where conditions are extreme we recommend fixing lighting or camera placement at source, because controlling the input is usually cheaper and more reliable than asking the model to compensate for everything.
How do you keep medical imaging HIPAA compliant?
Medical imaging work runs under strict controls: encrypted data handling, secure and access-controlled training environments, DICOM-aware pipelines, and full audit trails. We work with your healthcare IT team to meet HIPAA and your internal data-governance rules, and you retain ownership of patient data and the trained model.
Can the model be retrained as new data comes in?
Yes. We set up MLOps pipelines with monitoring for accuracy drift and scheduled or triggered retraining, so the model improves as you collect more images and as production conditions shift. Updates roll out gradually without interrupting the running system.
Which industries do you build computer vision for?
Mainly manufacturing for quality inspection and safety monitoring, healthcare for imaging and diagnostics support, and retail and e-commerce for product recognition and shelf monitoring, plus agriculture for crop and livestock analysis.
