Tbrain offers a managed data and evaluation platform, Expert OS, that combines expert knowledge bases, automated LLM‑as‑a‑judge evaluation, and agentic workflow orchestration for building and maintaining high‑quality training pipelines. The service provides versioned reference guides, real‑time audit dashboards, and on‑demand domain expert pods to help frontier AI labs and enterprise teams scale complex data programs without building infrastructure in‑house.
Funding
Funding not disclosed
Founders
Product
Problem
Frontier AI teams often lack the infrastructure and specialized expertise to collect, evaluate, and manage high‑quality training data for agentic systems, especially in complex domains such as robotics, medicine, or code generation. Building and maintaining these pipelines in‑house is time‑consuming, error‑prone, and difficult to audit.
Solution
Tbrain provides a managed data and evaluation platform called Expert OS that integrates expert knowledge bases, automated LLM‑as‑a‑judge evaluation, and agentic workflow loops into a single production system. The platform lets teams define versioned reference guides for each agent, run AI‑driven pre‑screening of outputs, and route human feedback back into the loop for continuous improvement. All operations are tracked in real‑time dashboards with audit logs, provider routing, and cost telemetry, enabling transparent, auditable pipelines. Domain‑specific expert pods supply the subject‑matter expertise required for high‑stakes tasks, while custom software tools automate annotation, quality control, and benchmark creation. By delivering these capabilities as a turnkey service, Tbrain allows AI labs to scale complex data programs without building the underlying infrastructure themselves.
Target Audience
Primary customers are frontier AI research labs and enterprise teams developing agentic AI products that require domain‑specific, high‑quality training data, evaluation pipelines, and managed expert workflows.
Features
- Knowledge workspace with curated, versioned reference guides scoped per agent and project
- LLM‑as‑a‑judge layer that automatically evaluates model outputs before human review
- Agentic workflow builder (drag‑and‑drop, Temporal‑backed) supporting 24 node types including auto‑QC, branching, and human review
- Real‑time control room with live audit logs, project health scores, and failure‑rate alerts
- Per‑project provider configuration with fallback chains, rate‑limit enforcement, and cost/token telemetry
- Batch assignment and reviewer queue UI that tracks KPIs, status pills, and personal task dashboards
- Integrated benchmark creation and evaluation harnesses for multi‑step reasoning and tool‑use tasks