Therubric offers a domain‑specific data infrastructure that captures expert judgment, procedural skill, and tacit knowledge across modalities—egocentric video, audio‑visual streams, and multilingual text—and converts it into structured, auditable datasets for AI. Its platform provides workflows to extract training signals, build complex reasoning environments, and create epistemic evaluation frameworks, enabling AI models in regulated sectors like healthcare, finance, and law to be trained, evaluated, and monitored with reliable, expert‑grounded evidence.
Funding
Funding not disclosed
Founders
Product
Problem
Many high‑stakes domains such as healthcare, finance, and law rely on expert judgment, procedural skill, and tacit knowledge that are not captured in structured data. This “non‑verifiable knowledge” makes it difficult to create reliable training signals, evaluation criteria, and reward functions for AI systems, limiting their applicability in real‑world decision‑making.
Solution
Therubric provides a domain‑specific data infrastructure that converts credentialed human judgment into structured, auditable intelligence for AI. The platform captures expert actions, gestures, speech, and multimodal context through egocentric video, audio‑visual streams, and multilingual inputs, then organizes these signals into cross‑modal datasets. It offers workflows for extracting training signals, building complex reasoning environments, and designing epistemic evaluation frameworks where ground truth is contested. By managing a vetted network of licensed experts and automating data onboarding, Therubric enables AI models to be trained, evaluated, and monitored with reliable, domain‑grounded evidence.
Target Audience
Primary customers are AI teams and organizations operating in regulated or high‑complexity sectors—such as healthcare, life sciences, finance, legal, and industrial automation—that require structured expert knowledge to develop trustworthy AI systems.
Features
- Multimodal capture pipelines for egocentric video, synchronized audio‑visual streams, and multilingual text to record expert behavior in situ
- Structured workflows that transform raw expert actions into training signals, reward models, and evaluation metrics
- Epistemic evaluation framework for assessing AI performance in domains lacking definitive ground truth
- Integrated expert network tooling for rapid sourcing, verification, and onboarding of licensed professionals
- Cross‑modal dataset generation that aligns voice, image, gesture, and action data for post‑training model integration
- APIs and monitoring tools to apply captured expert reasoning throughout model training, validation, and production phases