Guideline first
The labelling guideline written and agreed before volume, with the edge cases named. Most annotation disputes are guideline gaps that nobody wrote down.
AI Training Data & Annotation
Training data is only as good as its reviewers
How the labelling is run
Annotation quality is a process question. Two competent people labelling the same image will disagree at the edges, and without a written guideline that resolves those edges, the disagreement ends up in your dataset as noise the model has to learn around.
StaffCloud works from a written guideline agreed before the first batch, with a review tier above the labellers and a measured agreement rate between them. When a defect recurs, the guideline changes — because a recurring defect is an instruction problem, not an operator problem.
The team is in-house and named, not a crowd. That matters for anything sensitive, anything that needs domain judgement, and anything where you want the same people to still be there in month six.
Capabilities
Tiered review
A review layer above the labellers, with a defined sample rate and a documented escalation path for cases the guideline does not settle.
Measured agreement
Inter-annotator agreement measured and reported, so quality is a number you can watch rather than an assurance you have to trust.
In-house and named
The same team on your project month after month, under your confidentiality terms — which is what makes sensitive and domain-specific work possible at all.
How the engagement runs
-
01
Guideline and pilot
A written guideline drafted with you, then a pilot batch labelled against it. The pilot exists to break the guideline while it is still cheap to change.
-
02
Calibration
Labellers calibrated against gold examples, with agreement measured before volume starts rather than discovered during it.
-
03
Production with review
Volume labelling with the agreed review tier and sample rate, and edge cases escalated rather than guessed.
-
04
Measure and revise
Agreement and defect types reported on a cadence, with the guideline revised where the data argues with it.
What you are actually paying for
Not labels. A repeatable process that produces the same label for the same case in month six as it did in week one.
- A guideline that has survived contact with real edge cases
- An agreement rate you can see rather than a claim you have to accept
- The same reviewers, under your terms, for the length of the project
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 StudioMulti-type labelling and review
- SuperAnnotateAnnotation and QA workflows
- LabelboxDataset and review management
- AudacityAudio segmentation and review
- PythonValidation, sampling and agreement scoring
- Your annotation platformWe work in yours where you have one
- Excel / Google SheetsGuideline tracking and defect logs
- Secure file transferDataset handling under your terms
- Your VPN or VDIWhere data may not leave your environment
What sits under AI Training Data & Annotation
4 capabilities, each staffed and documented separately. Pick the one closest to the work you need covered.
AI Data Annotation (Image & Video)
Outsource AI data annotation for images and video: bounding boxes, segmentation, keypoints and frame-by-frame labeling at scale.
Explore this serviceAI Data Annotation (Audio & Multimodal)
AI data annotation service for audio and multimodal data: transcription, speaker tagging, sentiment and paired text-image-audio labeling.
Explore this serviceAI Catalog Data Quality Auditing
An AI data quality audit that finds catalog substitutions, mislabeled records and annotation drift before they ever reach your model.
Explore this serviceLLM Data Labeling & Model Evaluation
LLM data labeling service covering RLHF preference ranking, prompt and response review, red teaming and scored model evaluation.
Explore this serviceQuestions 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 usWe will not quote a number here, because the honest answer depends on the task, the guideline and what you count as an error. What we will do is measure agreement on your pilot and report it — a real figure from your data rather than a marketing one from someone else's.
No. It is a named in-house team under your confidentiality terms. Crowd platforms are cheaper per unit and right for some simple, high-volume tasks; they are the wrong answer for domain judgement, sensitive data or anything needing consistency over months.
Yes, where the data may not leave it — your annotation platform, your VDI or your VPN. That constrains tooling and it is worth raising early rather than at contract stage.
They escalate rather than get guessed, and the resolution goes back into the guideline. A guideline that never changes during a project is a guideline nobody is reading.
Handled under your terms — named team, scoped access, your platform or transfer method. We will work to your security requirements rather than publish our own and hope they match.
Small enough to pilot is the only real requirement. A pilot batch is genuinely useful even if the full project never happens, because it usually exposes guideline problems you would otherwise find at volume.
Where it helps and where you agree to it — model-assisted pre-labelling with human review is often the right economics on high-volume, low-ambiguity tasks. We will tell you where it is being used rather than present it as hand work.