Train better AI with high-quality labelled data
A comprehensive annotation platform supporting images, video, text, audio, LiDAR, and 3D point clouds. AI-assisted workflows, expert annotators, and enterprise-grade quality assurance — all in one platform.
Annotate any data type
One platform for every modality. Switch between data types or build cross-modal datasets seamlessly.
Image Annotation
- Bounding boxes
- Polygons & polylines
- Semantic segmentation
- Instance segmentation
- Keypoints & landmarks
- Image classification
Video Annotation
- Object tracking across frames
- Action recognition
- Event labelling
- Frame-by-frame segmentation
- Activity classification
- Temporal annotations
Text & NLP
- Named entity recognition (NER)
- Sentiment analysis
- Intent classification
- Document classification
- Relationship extraction
- Multi-lingual annotation
Audio & Speech
- Speech-to-text transcription
- Speaker diarization
- Audio event tagging
- Sentiment in audio
- Multi-language support
- Phoneme labelling
3D & LiDAR
- 3D bounding cuboids
- Point cloud segmentation
- Sensor fusion (camera + LiDAR)
- Autonomous driving datasets
- Indoor scene annotation
- Multi-frame tracking
Sensor & Time-Series
- IoT sensor data labelling
- Time-series anomaly detection
- Medical signal annotation
- Industrial telemetry
- Multi-channel synchronization
- Custom sensor formats
Built for scale, accuracy, and speed
Enterprise-grade features that turn raw data into AI-ready training datasets.
AI-Assisted Labelling
Pre-labelled predictions from foundation models accelerate annotation by 10x. Human annotators verify and refine, dramatically reducing labelling cost and time.
Custom Workflows
Design multi-stage labelling pipelines with review, consensus, and gold-standard validation. Configure approval flows that match your quality requirements.
Expert Annotators
Access trained domain experts for medical imaging, autonomous driving, satellite imagery, and specialized industries. Or bring your own internal team.
Quality Assurance
Built-in QA tools with inter-annotator agreement metrics, consensus voting, gold-standard tasks, and statistical sampling for verifiable quality.
Enterprise Security
SOC 2 compliant, GDPR ready, with optional on-premise deployment. End-to-end encryption, role-based access control, and full audit logs.
Active Learning Loop
Integrates with your model training pipeline. Identifies edge cases, prioritizes uncertain samples, and continuously improves dataset quality.
Global Workforce
24/7 annotation operations across multiple time zones with native language support for over 40 languages and regional domain expertise.
Real-Time Analytics
Track project progress, annotator performance, throughput, and quality metrics with comprehensive dashboards and exportable reports.
Powering AI across industries
Trusted training data for the most demanding AI applications.
Computer Vision
Object detection, scene understanding, OCR, facial recognition, and visual search models for retail, security, and automotive applications.
Autonomous Driving
Sensor fusion datasets combining camera, LiDAR, and radar data with detailed 3D cuboid annotations and lane segmentation.
Document AI
Invoice processing, contract analysis, form extraction, and intelligent document understanding for enterprise automation.
Generative AI Training
RLHF datasets, instruction tuning, preference comparisons, and red-teaming evaluations for large language models.
Medical Imaging
DICOM annotation for radiology, pathology slides, surgical video, and clinical trial datasets with HIPAA-aligned workflows.
Geospatial AI
Satellite and aerial imagery annotation for agriculture monitoring, urban planning, disaster response, and defense applications.
Your data, protected at every step
Industry-leading security and compliance for enterprises handling sensitive training data.
- SOC 2 Type II compliant infrastructure
- GDPR and HIPAA aligned workflows
- End-to-end encryption at rest and in transit
- Role-based access control (RBAC)
- Complete audit logs and access tracking
- On-premise and air-gapped deployment available
Frequently Asked Questions
What is the Aqylon AI Data Labelling Platform?
Aqylon AI Data Labelling is a comprehensive annotation platform that supports images, video, text, audio, LiDAR, 3D point clouds, and sensor data. It combines AI-assisted labelling tools, expert human annotators, and rigorous quality assurance to produce high-quality training data for AI models.
How does AI-assisted labelling work?
Our platform uses foundation models to generate pre-labelled predictions for your data. Human annotators then verify, correct, and refine these predictions instead of starting from scratch. This typically reduces labelling time by 10x while maintaining or improving accuracy.
What data types do you support?
We support image annotation (bounding boxes, polygons, segmentation, keypoints), video annotation (object tracking, action recognition), text and NLP (NER, classification, sentiment), audio and speech, 3D point clouds and LiDAR, and time-series sensor data.
Can I deploy on my own infrastructure?
Yes. The platform supports cloud, on-premise, and hybrid deployments. For sensitive industries like healthcare, defense, and finance, we offer fully air-gapped on-premise installations with no external data transmission.
How do you ensure quality?
Quality is enforced through multi-stage review workflows, inter-annotator agreement metrics, consensus voting, gold-standard task validation, statistical sampling audits, and real-time analytics. Domain experts provide additional review for specialized datasets.
Is the platform secure for sensitive data?
Yes. The platform is SOC 2 compliant and GDPR ready, with end-to-end encryption, role-based access control, complete audit logs, and HIPAA-aligned workflows for healthcare data. We also support custom security policies and on-premise deployment for maximum data sovereignty.
Do you provide annotators or do I bring my own team?
Both options are available. We have a global network of trained annotators with domain expertise across industries. Alternatively, you can use the platform with your own internal annotation team or a hybrid model combining both.