Inconsistent labels
Different annotators interpret the same guideline differently.
Human data annotation for AI and machine learning
Accurate labels from trained people, delivered in batches with quality checks you can see. Text, image, video and audio annotation for training and evaluating AI models.
Quoted per project · How pricing works
What it is
AI data annotation, explained
Models learn from the labels you give them. Inconsistent or careless annotation quietly limits model quality, and fixing it later costs far more than getting it right first. TestLancer runs annotation projects with clear guidelines, a paid pilot batch to agree standards, and review layers that measure accuracy before data reaches you.
Who it's for
- AI and machine learning startups
- Research teams building datasets
- Companies fine-tuning models on their own data
- Computer vision projects
- Speech and language technology teams
- Agencies with AI client projects
Problems solved
What this fixes
No visibility into quality
You only find labeling errors after training fails.
Hard-to-scale teams
Hiring and managing annotators in-house takes time.
Language and context gaps
Some data needs native speakers or local knowledge.
What's included
What you get with AI data annotation
Text annotation
Classification, sentiment, entity tagging and intent labeling.
Image labeling
Classification, bounding boxes, polygons and keypoints.
Video annotation
Frame and event labeling, object tracking.
Audio transcription
Transcripts, speaker labels and timestamps.
Guideline development
We help turn your rules into clear instructions with examples.
Quality assurance
Gold-standard checks, review sampling and agreement scores.
How it works
From brief to results
- Share the projectData type, volume, labels and quality target.
- Pilot batchA small batch to agree guidelines and accuracy.
- Workforce assignedTrained members matched to your language and domain.
- Production with QAWork in batches with review and consistency checks.
- Deliver and approveData in your format, with quality metrics.
Deliverables
- Labeled data in your required format (CSV, JSON, COCO, YOLO or custom)
- Accuracy and agreement metrics per batch
- Edge cases and guideline questions logged
- Batch delivery schedule
- Final quality summary
AI data annotation: frequently asked questions
Which tools do you use?
We can work in your annotation platform or in widely used tools such as Label Studio or CVAT, depending on the project.
How do you measure quality?
Through gold-standard questions, reviewer sampling and agreement between annotators. You see the metrics for every batch.
Can you handle sensitive data?
Tell us your requirements. We can limit access, remove personal data before annotation and add confidentiality terms. Some highly sensitive data may not be suitable for a distributed workforce.
What's the minimum project size?
We start with a pilot batch so you can check quality before committing to volume.
For businesses & agencies
Start your project with real people.
Testing, research, AI data or skilled freelance work. Tell us what you need and get a clear scope and price before anything starts.
For testers & freelancers
Turn your skills into opportunities.
Testing, research, AI tasks and freelance work. One free account. Rewards for approved, genuine work.
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