Data Annotation
Training data your models can actually trust.
High-accuracy, human-in-the-loop labeling for computer vision, NLP, and LLM pipelines — built on rigorous quality review, not just annotator throughput.
- Target labeling accuracy
- 98%+
- Annotation types supported
- 14+
- Multi-pass QA on every dataset
- Human-in-the-loop
Visual workflow
From raw data to model-ready dataset
Every dataset we deliver passes through the same six-stage quality pipeline.
- Raw DataImages, video, text & audio ingested
- AnnotationLabeled by trained, specialized annotators
- Quality ReviewMulti-pass review against guidelines
- ValidationInter-annotator agreement scoring
- DatasetStructured, versioned & delivered
- AI TrainingPowers your model's next iteration
The problem
Model quality is a training-data problem in disguise
Most AI underperformance traces back to the dataset, not the model — inconsistent labels, missed edge cases, and no real quality process behind the labeling work.
- Inconsistent labeling across annotators quietly degrades model accuracy
- No inter-annotator agreement scoring means errors go undetected until production
- Generic labeling vendors don't understand your specific taxonomy or edge cases
- Sensitive data handled without proper confidentiality controls creates real risk
The solution
Annotation built like a QA discipline, not a data-entry task
We treat annotation as a quality-controlled pipeline — trained specialists, multi-pass review, and measurable agreement scoring behind every dataset we deliver.
Specialist annotators
Trained on your taxonomy and edge cases, not generic labeling guidelines.
Reviewed, not just labeled
Every dataset passes multi-layer quality review before delivery.
Secure by default
Confidential and regulated data handled under strict access controls.
Capabilities
Annotation types we cover
One team spanning every modality your model needs to learn from.
Image annotation
Labeling across large, diverse image sets for vision models.
Video annotation
Frame-by-frame labeling for temporal and motion-based models.
Bounding boxes
Fast, precise object localization for detection models.
Polygon annotation
Tight, irregular-shape boundaries for precise object outlines.
Semantic segmentation
Pixel-level class labeling across an entire scene.
Instance segmentation
Pixel-level labeling that distinguishes individual object instances.
Keypoints
Pose, landmark, and skeletal point annotation.
Classification
Category and attribute tagging at image, frame, or document level.
OCR
Text extraction and transcription from scanned documents and images.
NLP annotation
Entity, intent, sentiment, and relation labeling for language models.
Audio annotation
Transcription, speaker labeling, and sound-event tagging.
Autonomous vehicle data
LiDAR, radar, and multi-camera labeling for perception stacks.
Technology
Technology we use
Purpose-built tooling matched to your schema, not a one-size-fits-all interface.
Annotation platforms
- Label Studio
- CVAT
- Custom Annotation Tooling
Data infrastructure
- Snowflake
- Secure Cloud Storage
Quality tooling
- Inter-annotator agreement scoring
- Gold-standard test sets
Architecture
How a dataset moves from raw data to model-ready
The same six-stage quality pipeline behind every dataset we deliver.
01
Raw data intake
Images, video, text, or audio ingested from your systems.
02
Annotation
Trained specialists label against your taxonomy and guidelines.
03
Quality review
Multi-pass review catches inconsistency and edge-case errors.
04
Validation
Inter-annotator agreement scoring confirms label reliability.
05
Dataset delivery
Structured, versioned datasets delivered in your required format.
Use cases
Where we've applied this
Automotive
Autonomous vehicle perception
LiDAR and multi-camera datasets for self-driving perception stacks.
AI & Software
LLM fine-tuning & RLHF
Preference-ranking and instruction data for model alignment.
Manufacturing
Manufacturing quality inspection
Labeled defect imagery for computer-vision inspection models.
Healthcare
Medical imaging annotation
Precisely labeled imagery for healthcare AI development.
Process
How we deliver a dataset
- 01
Define the schema
Align on taxonomy, edge cases, and quality bar before labeling starts.
- 02
Pilot & calibrate
Small batch labeled and reviewed to calibrate annotator agreement.
- 03
Scale annotation
Full dataset labeled by a trained, quality-monitored team.
- 04
Validate & deliver
Final QA pass and structured delivery in your required format.
Benefits
What a real QA process buys you
Accuracy
Multi-pass review and gold-standard testing keep error rates low.
Quality
Specialists trained on your exact taxonomy, not generic guidelines.
Scalability
Teams scale from pilot batches to millions of labeled assets.
Consistency
Inter-annotator agreement scoring keeps labeling uniform at scale.
Confidentiality
Access-controlled environments for sensitive and regulated data.
Human-in-the-loop QA
Every dataset is reviewed by people, not just automated checks.
Keep exploring
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Scalable outsourced teams for customer support, back-office, and operations workflows.
ExploreFAQ
Frequently asked questions
We use multi-pass review, gold-standard test sets, and inter-annotator agreement scoring, typically achieving 98%+ accuracy on production datasets.
Ready for training data your model can actually trust?
Tell us your modality and taxonomy — we'll scope a pilot batch to calibrate quality before you commit to scale.