Mirel provides an AI-powered platform for automated data labeling and synthetic data generation for machine learning applications. The system accelerates model training pipelines by delivering high-quality, labeled datasets at scale. This capability enables faster deployment of computer vision and perception systems across various industries.
Funding
Funding not disclosed
Founders
Product
Problem
Creating large, accurately labeled datasets for computer vision and perception models is labor-intensive, costly, and often requires domain experts. Many organizations lack sufficient annotated data to train high‑performing models, leading to delayed product releases and suboptimal system performance. Additionally, generating realistic synthetic data at scale remains a technical bottleneck for simulation‑driven development.
Solution
Mirel offers an AI‑driven platform that automates the end‑to‑end data labeling workflow and produces synthetic datasets on demand. The system leverages pretrained vision models and active‑learning loops to suggest annotations, which are then refined through a lightweight human‑in‑the‑loop interface, dramatically reducing manual effort. Parallel to labeling, a generative pipeline creates photorealistic synthetic images, point clouds, and video sequences that can be customized for specific sensor configurations and environmental conditions. All data are versioned, quality‑scored, and exported via RESTful APIs or SDKs to integrate seamlessly with existing ML pipelines. By delivering high‑quality, labeled data at scale, Mirel shortens model training cycles and accelerates deployment of perception systems across diverse industries.
Target Audience
The primary customers are machine‑learning engineers, data‑science teams, and computer‑vision developers in sectors such as autonomous vehicles, robotics, industrial inspection, and medical imaging who need rapid, scalable dataset creation and augmentation.
Features
- AI‑assisted annotation engine with active‑learning prioritization to focus human review on uncertain samples
- Automated synthetic data generator using diffusion and GAN models for images, LiDAR point clouds, and video streams
- Domain adaptation tools that blend synthetic and real data to improve model generalization
- End‑to‑end data versioning and provenance tracking compliant with ISO 27001 and GDPR
- REST API, Python SDK, and CI/CD plugins for direct integration with training pipelines and MLOps platforms
- Built‑in quality metrics (annotation confidence, synthetic realism scores) and batch validation dashboards
- Support for multi‑modal sensor suites (camera, radar, depth) and customizable environment parameters (weather, lighting, occlusion)