YOLO specialists for your dedicated team.

Where YOLO fits in a long-term product team.

Single-pass detection is what makes real-time video practical. Two-stage detectors are more accurate on difficult cases but too slow for frame-rate processing, and for most uses, such as counting, tracking and safety monitoring, speed matters more than the last few points of accuracy.

Custom detection needs annotated data, and the annotation is the project. Thousands of images with accurate bounding boxes is a significant effort, and annotation quality directly determines model quality: inconsistent boxes produce a model that is inconsistent in exactly the same way.

What your dedicated team does with YOLO.

Deployment target shapes the whole design. A model running on a server can be large; one running on an edge device has hard memory and compute limits, which make quantisation or a smaller variant essential.

Deciding that before training is basic sequencing, and part of the scoping described in how it works.

Common YOLO use cases.

• Detection at video frame rate Single-pass inference fast enough for live streams.
• Annotation quality treated as the deliverable Consistent bounding boxes, because the model inherits their inconsistency.
• Model sized for the deployment target Edge constraints decided before training.

Adding YOLO skills to your team.

Object detection work sits with the AI engineers on your team, with annotation effort scoped realistically as the largest part of the work.

At Azendo, YOLO work sits with a dedicated AI engineering team. You can add YOLO capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.