Custom vision models trained on your images, not a generic API applied to 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, not the other way round. 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 — including the edge cases that a benchmark dataset never covers.
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 a single 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 — not a single aggregate mAP score. Explicit review of failure modes with domain experts before anything ships.
Packaging the model for its actual runtime target — cloud inference or an edge device — with a clear spec for pre/post-processing so production engineering can integrate it without guesswork. 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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