Data Foundation for High-Accuracy AI
Powering production-grade models with meticulously curated, human-validated datasets. Algorithms learn nothing without ground truth.
From raw data to model-ready ground truth
One pipeline for every modality, with humans at the point where judgment matters.
The Synthetic Limit
Pure automated labeling creates compounding feedback loops of error.
Model Degradation
Self-trained models eventually plateau. Edge cases like occlusions, adverse weather in CV, or sarcasm in NLP require nuanced human judgment that machines cannot inherently deduce.
Lack of Domain Expertise
Generic crowdsourcing platforms fail miserably when tasking medical imaging or financial document extraction. High-accuracy AI requires domain-trained subject matter experts.
Our Annotation Capabilities
Comprehensive modality coverage managed by our proprietary platform.
Image Annotation
Precision bounding boxes, polygons, and semantic segmentation for computer vision.
Video Annotation
Object tracking, event tagging, and temporal segmentation across frames.
Text & NLP
Entity extraction (NER), sentiment analysis, and intent classification.
Audio & Speech
High-fidelity transcription, speaker diarization, and emotion recognition.
LiDAR & 3D
3D Point cloud annotation, cuboids, and sensor fusion labeling.
Product Categorization
E-commerce attribute tagging, taxonomy mapping, and visual search training.
Generative AI & Editing
Natural language image editing, RLHF, and model output ranking.
AR / VR
Scene understanding, depth estimation, and 6DoF object pose estimation.
Custom Workflow
We design custom annotation architecture for edge cases.
Human-in-the-Loop Workflow
Automated models execute the first pass; our human experts resolve the exceptions. This continuous feedback loop ensures that your AI pipeline learns from novel edge cases without corrupting its foundational weights.
- Exception handling APIs
- Active learning integration
Multi-Tier QA Framework
Our 99.5% quality guarantee isn't a marketing claim; it's a contractual SLA backed by a rigorous 3-tier validation process involving automated checks, consensus scoring, and physical gold-standard auditing.
- Automated schema validation
- Blind consensus (inter-annotator agreement)
Build your model on a solid foundation.
Get sample annotations back within 48 hours to validate our speed out-of-the-gate.
Start a Pilot