Ayta
Ayta provides a unified data pipeline for AI teams that need custom, real-world training data beyond off-the-shelf datasets. The company handles sourcing, field capture, annotation, quality assurance, and delivery across image, video, audio, text, and multimodal formats, including deploying trained collectors on location. Its services extend to RLHF preference ranking and model evaluation for teams fine-tuning generative systems.
- Artificial Intelligence
Funding
Founders
Founder details are not available yet.
Product
Problem
AI teams building foundation models, computer vision systems, robotics programs, and voice AI often exhaust off-the-shelf datasets that lack real-world coverage and fail to meet their specific demographic, device, and environmental requirements. Sourcing high-quality training data across multiple modalities typically requires coordinating a patchwork of vendors, which fragments quality control and slows delivery.
Solution
Ayta operates as a single connected data pipeline covering sourcing, field capture, annotation, quality, and delivery. The company builds custom collection operations around each client's specification, deploying trained collectors on location when data must exist in the real world rather than merely annotating existing assets. Trained annotators handle labeling across bounding boxes, segmentation, transcription, and classification, while automated checks plus human review at every stage verify quality before delivery. Ayta also provides RLHF preference ranking, model evaluation, and structured dataset handoffs with raw assets, QA reports, metadata, and documentation packaged for direct integration into customer pipelines.
Target Audience
Primary customers are foundation model labs, computer vision teams, robotics programs, and voice AI teams that require custom real-world training data beyond what off-the-shelf datasets provide.
Features
- Digital and in-field data collection across image, video, audio, text, and multimodal formats sourced to exact demographic, device, and environment specifications
- Field operations with trained collectors deployed on location for recruitment and on-site capture of real-world data
- Human-in-the-loop annotation covering bounding boxes, segmentation, transcription, and classification across all modalities
- Multi-layer QA combining automated checks with human review at every pipeline stage
- RLHF preference ranking, response evaluation, and structured model assessment for generative system fine-tuning
- Dataset delivery with raw assets, QA reports, metadata, and documentation packaged to match customer pipeline requirements