Bounding boxes and polygons
2D boxes, rotated boxes and polygon outlines to the precision your task actually needs, which is a decision worth making explicitly rather than defaulting to maximum.
AI Data Annotation (Image & Video)
Labelled to the guideline, reviewed before delivery
What this covers
Image and video labelling is where most computer-vision projects spend their budget and lose their quality. The work itself is straightforward; the difficulty is consistency — the same occlusion rule, the same class boundary, the same handling of the ambiguous case, applied by several people across months.
That consistency comes from the guideline and the review layer, not from the labellers being careful. A careful labeller with an ambiguous guideline still produces inconsistent data.
StaffCloud runs image and video work with a written guideline, calibration against gold examples, a defined review sample and measured agreement. Edge cases escalate and the resolution goes back into the guideline.
What the team does
Semantic and instance segmentation
Pixel-level masks with class and instance boundaries defined in the guideline, including how touching and overlapping objects are handled.
Keypoints and landmarks
Pose, facial and object landmark annotation to a defined skeleton and visibility convention.
Video and object tracking
Frame-by-frame annotation with consistent identity across frames, including the re-entry and occlusion rules that decide whether tracking data is usable.
Review and calibration
Labellers calibrated on gold sets, a defined sample reviewed, and agreement measured rather than assumed.
Guideline maintenance
The guideline treated as a living document, revised as edge cases surface, with changes versioned so a dataset can be read against the rules that produced it.
How the engagement runs
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01
Guideline drafting
Classes, boundaries, occlusion and ambiguity rules written with you, with the edge cases named up front.
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02
Pilot and calibration
A pilot batch labelled and reviewed together, agreement measured, and the guideline revised where the data disagrees with it.
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03
Production
Volume labelling with the agreed review tier, edge cases escalated rather than resolved locally.
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04
Delivery and reporting
Data delivered in your format with agreement and defect reporting alongside it.
What separates usable data from expensive data
Not effort per frame. Whether the same case gets the same label in week one and month six.
- A guideline that has been tested against real ambiguity
- Agreement measured on your data, not quoted from elsewhere
- Edge cases resolved once and written down
Platforms and tools we work in
The pod works inside your stack. These are the systems this team uses day to day — if yours is not listed, we will learn it.
- CVATImage and video annotation
- Label StudioLabelling and review workflows
- SuperAnnotateAnnotation and QA
- LabelboxDataset management
- Your annotation platformWe work in yours where you have one
Questions buyers ask
The things worth knowing before the first call.
Not sure this is the right scope?
Describe the workload in a sentence and we will point you at the right team — or tell you honestly if it is not something we should take on.
Ask usStaffCloud measures accuracy on your pilot and reports the number you actually get, rather than quoting a figure before seeing the task.
Any accuracy rate stated in advance is someone else's data on someone else's problem, and it tells you nothing useful about yours. Measuring on a pilot gives you a real number to plan against and a baseline to hold the work to as volume grows.
Yes, and it is often the better option — your platform, your review workflow, and your data never leaving your environment.
It does affect throughput, since the team works at the speed your tooling allows rather than one tuned for annotation. StaffCloud raises that early so the trade-off between control and pace is a decision you make rather than a surprise later.
They escalate rather than being decided by whoever happens to have the file open. The guideline names the ambiguity types the team expects, and anything new goes up.
Each resolution is written back into the guideline, so the same case is handled the same way by everyone from then on. That is what keeps a dataset internally consistent as it grows, which matters more to a model than any single judgement call.
Where it genuinely helps and where you have agreed to it. On high-volume classes with little ambiguity, it is good economics.
On subtle boundaries it does the opposite: it nudges the labeller toward the model's own mistakes and quietly bakes them into your training data. StaffCloud will tell you which case your task falls into rather than applying it across the board because it is faster.
Whatever your pipeline consumes — COCO, YOLO, Pascal VOC or a schema of your own.
Converting between formats is the easy part. Agreeing what the schema actually means — where one label ends and the next begins — is the part that decides whether the data is usable, so StaffCloud settles that in the guideline step before any volume is produced.
Yes, within the limit that new annotators need calibrating before their output is usable. StaffCloud plans the ramp around that rather than around headcount alone.
Adding people faster than they can be calibrated is how a dataset's quality drops halfway through a project without anyone noticing until training. Scaling at a pace the calibration supports is what keeps the second half as good as the first.