VoxelEon combines AI-assisted pre-annotation with structured multi-annotator QA workflows and enterprise RBAC governance — delivering high-fidelity labeling across 2D, 3D, and video modalities at scale.
VoxelEon spans two integrated product surfaces — a standalone labeling platform and a CVAT enterprise extension — unified under a single data intelligence framework.
Deployed foundation models — SAM ViT-H for interactive segmentation and YOLOv7 for real-time instance detection — accelerate annotation throughput and reduce manual effort on routine labeling tasks.
Intelligent vendor selection and workload distribution driven by multi-factor scoring — balancing cost, QA performance history, and real-time capacity. Difficult batches are identified via feature-vector analysis and routed accordingly.
Multi-stage QA workflows collect independent annotations per task and compute agreement scores. Labels meeting the consensus threshold are accepted; contested items are escalated through structured review and adjudication workflows.
Ingest datasets from GCS-hosted archives or local filesystems. Full audit logging per annotation event — annotator, timestamp, and QA scores recorded. Qdrant vector store enables batch difficulty scoring and smart job routing.
Reviewer corrections feed directly into vendor QA scoring. Underperforming vendors are automatically flagged for suspension. Leaderboards surface annotator performance trends across batches, enabling targeted quality interventions.
Five-tier role hierarchy (annotator → reviewer → manager → admin → owner). JWT-based auth with PBKDF2-SHA256 password hashing. Policy enforcement via OPA (Open Policy Agent). Full audit logging and GDPR-compliant data handling.
Every annotation batch passes through VoxelEon's three-tier QA scoring pipeline — driven by real threshold values enforced across all vendor workflows.
Annotations accepted and ingested. Vendor QA score updated positively. Batch proceeds to export.
Batch flagged for manual reviewer inspection. Corrections applied before acceptance.
Batch rejected, vendor QA score penalized. Persistent failures trigger automatic vendor suspension.
A carefully curated stack — every component chosen for performance, reliability, and commercial viability.
Enterprise capability without enterprise pricing. The annotation market finally has a platform built for the AI-first era.
| Feature | VoxelEon | Scale AI | CVAT | Snorkel |
|---|---|---|---|---|
| AI Auto-Annotation | ✓ SAM ViT-H + YOLOv7 | ✓ Basic | ✓ Limited | ✓ Research-grade |
| Smart Batch Routing | ✓ Cost + QA + Availability | ✗ Manual triage | ✗ None | ✓ Basic |
| Cost per Image | $0.50–$2.00 | $5–$15 | $1–$3 | $3–$10 |
| QA Scoring | ✓ Per-batch, threshold-driven | ✓ Opaque | ✗ None | ✓ Post-hoc |
| Consensus Engine | ✓ Automated ≥65% | ✗ Manual coord. | ✗ Manual | ✓ Custom dev |
| RBAC Governance | ✓ 5-tier + OPA policies | ✓ Basic roles | ✓ Limited | ✗ None |
| Deployment | Cloud + On-prem + Edge | Cloud only | Both | Cloud only |
| Advanced AI Models | Roadmap: SAM2 · DINO · YOLOv10 | ✓ Proprietary | ✗ None | ✗ None |
Detect pedestrians, vehicles, road signs, and lane markings across video streams with multi-annotator consensus meeting safety certification requirements.
Segment tumors, lesions, and anatomical structures in CT/MRI scans. HIPAA-compliant with full audit trails. QA workflows prioritize rare pathologies for radiologist review, while structured escalation ensures expert sign-off on edge cases.
Categorize, segment, and tag product images at scale. Qdrant-powered batch routing groups visually similar items for efficient reviewer workflows. QA scoring adapts to seasonal catalog shifts.
Map urban infrastructure, agricultural land use, and disaster zones. Process 50,000+ images/day with confidence-driven triage routing only novel patterns to human experts.
Defect detection on assembly lines and PCB inspection. Reviewer feedback drives vendor QA score adjustments. Fast batch throughput reduces time-to-label from days to hours.
Annotate terrain features, infrastructure changes, and environmental monitoring data. Scalable to government-grade compliance requirements with immutable audit trails.
Export your datasets and models at any time. No proprietary formats. No vendor lock-in.
All plans include dataset export in COCO, YOLO, Pascal VOC formats. Pay-per-image available at $0.50–$2.00/image with volume discounts.
Access the VoxelEon platform — built on real ML infrastructure, running now.
