Custom Computer Vision Development for Images, Video and Visual Data

Not always. Requirements depend on task complexity, class variation, pre-trained model suitability and
expected performance. We first inspect representative samples and determine whether transfer
learning, an existing API or custom training is appropriate.

Potentially. We evaluate hardware, model size, latency, connectivity, privacy and update requirements
before recommending on-device, edge or cloud deployment.

Yes. We can combine OCR, layout understanding, computer vision and validation logic to extract
structured information. Complex document workflows may be better served by the Intelligent
Document Processing service.

We use representative held-out data and review performance across classes, conditions and error types.
We also test capture quality, preprocessing, latency and the human or system response to uncertain
predictions.

Yes. We can review data coverage, annotation quality, leakage, model choice, thresholds, deployment
pipeline, inference performance and monitoring to identify the main limitations.

We define collection, consent, access, retention, storage, provider and processing controls around the
specific data and legal obligations. Sensitive use cases require additional review before scope is
accepted.

Possibly. We first assess resolution, angle, lighting, frame rate, compression and scene consistency
against the target task. A small capture study can show whether the current hardware is adequate.

We can design the annotation specification, workflow and quality checks, and coordinate annotation as
part of the engagement. Clear class definitions and reviewer agreement are essential to a useful dataset.

Yes, when hardware, model size, latency, power and update requirements support it. We compare edge,
cloud and hybrid deployment based on operating constraints rather than assuming one pattern.