Custom vision models trained on your images, addressing your problem.
General-purpose vision APIs recognise generic objects. They don't recognise the specific defect, product, or condition that matters to your operation.
Building a model that does means starting with the data problem, not the model: scoping what needs to be annotated and to what standard, building or configuring an annotation pipeline, and curating a training set that actually covers the variation the model will see in production — lighting, angle, occlusion, background clutter.
Architecture selection follows from the constraints. Object scale and density point toward a particular detector family. Inference latency and power budget decide whether the model runs on a GPU server or an edge device. Class imbalance and rare-event frequency shape the training strategy as much as the architecture itself.
We build and validate against your images and your definition of a correct detection.
How we approach it
Defining exactly what counts as a detection, what classes exist, and how edge cases and ambiguous instances should be labelled, before any image is annotated. Ambiguity here compounds through every downstream step.
Scoping the image sources needed to cover real production variation. Building or configuring an annotation workflow with quality control, and defining inter-annotator agreement checks so label noise doesn't quietly cap model performance.
Choosing between detector families, segmentation approaches, and transfer-learning starting points based on object scale, class count, and available compute. Augmentation strategy tuned to the specific conditions the model will face in the field.
Precision and recall broken down by class, object size, and known hard cases. Providing insights beyond a high-level single-model aggregate mAP score. Explicit review of failure modes with your domain experts and practitioners before shipping.
Packaging the model for its actual runtime target, will it be deployed into cloud inference or an edge device? With a clear spec for pre/post-processing so models can be integrated into existing production pipelines. Monitoring plan for performance drift as conditions change.
What the model sees
Detections on a frame
Bounding boxes with confidence scores — trained on your product, your camera angle, your lighting.
Tell us what you need detected and we'll scope it together — no obligation.
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