Image and video annotation
Bounding boxes, polygons, landmarks and other labels defined by the project specification.
Service 03 · AI and data operations
AI training data shaped by clear guidelines.
Support AI and machine-learning workflows with structured annotation, data preparation and quality review designed around your taxonomy, schema and acceptance criteria.
Guideline-led labellingSchema-aligned outputsQuality review workflows
Data workflows
Define the task, edge cases and quality checks.
Text and document data
Text classification, extraction and document labelling against agreed annotation rules.
Audio and language data
Transcription-related workflows, speech data labelling and multilingual dataset support where scoped.
Validation and QA
Sample review, edge-case tracking, consistency checks and structured feedback to refine guidelines.
A responsible starting point
Agree the annotation standard before scaling.
Define the taxonomy, output schema, representative sample, quality thresholds, data handling rules and escalation process. Sensitive data and security requirements should be addressed before transfer.
Common questions
Planning an annotation programme
What should an annotation brief include?
Include data types, task definitions, label taxonomy, output format, sample volume, edge cases, quality criteria, timeline and data handling requirements.
Can guidelines be refined during a pilot?
A pilot can expose ambiguous cases and help teams refine instructions before a broader production workflow is confirmed.
Do you guarantee a particular accuracy level?
Quality targets should be agreed against the task, sample and evaluation method. Any target or service commitment must be confirmed in the project scope.
Need structured data for an AI workflow?
Tell us about the data type, annotation task and quality requirements.
Send a project enquiry ↗