Datoric builds and manages data programs that supply multimodal AI systems with production‑ready datasets. It handles the full pipeline—from protocol design and data collection across voice, video, egocentric, robotics, and human interaction, to validation and delivery—enabling AI developers to meet complex data requirements efficiently.
Funding
Funding not disclosed
Founders
Product
Problem
AI developers building multimodal models often lack access to large, high-quality datasets that span voice, video, egocentric, robotics, and human interaction domains. Creating such datasets requires extensive protocol design, data collection, validation, and delivery infrastructure, which many teams cannot efficiently manage in‑house.
Solution
Datoric operates as a dedicated human data lab that designs, executes, and delivers production‑ready multimodal datasets tailored to complex AI requirements. The company manages the full data pipeline—from protocol specification and large‑scale collection to rigorous validation and secure delivery—ensuring consistency and quality across diverse modalities. By handling the logistical and technical challenges of data acquisition, Datoric enables AI teams to focus on model development and iteration. The service includes curated data pipelines that meet predefined specifications, reducing time‑to‑data and mitigating risks associated with noisy or incomplete datasets. Clients receive validated, ready‑to‑train collections that support the development of more capable and reliable multimodal AI systems.
Target Audience
Primary customers are AI research labs, enterprise AI teams, and startups developing multimodal machine‑learning models that require specialized, high‑quality training data.
Features
- End‑to‑end dataset production covering voice, video, egocentric, robotics, and human interaction data
- Custom protocol design aligned with client‑specified data requirements and quality standards
- Large‑scale collection operations with managed participant recruitment and sensor deployment
- Multi‑stage validation processes, including annotation quality checks and modality consistency verification
- Secure data delivery via encrypted transfer and format conversion for immediate integration into training pipelines
- Scalability to support iterative data updates and expansion as model needs evolve