Ooak Data

Teams building AI agents, including frontier AI labs, enterprise AI teams, and AI startups, need to evaluate and train models on realistic workflows, not just single-turn Q&A. Synthetic benchmarks test what models can do in theory but not what they do in practice, and most evaluation frameworks are text-only and single-turn rather than multimodal and multi-step. Building realistic evaluation infrastructure from scratch, including sourcing real organizational data across tools like Slack, Gmail, Notion, Jira, SharePoint, and Teams, is a significant undertaking. Without this, teams risk testing models against benchmarks that do not reflect actual multi-tool, multi-step enterprise work.

Ooak Data sources real enterprise data directly from companies, spanning documents, communications, and tools, with full organizational context preserved. It runs this data through an automated multimodal anonymization pipeline that transforms names, dates, and proprietary content into 'digital twins' while preserving structure, relationships, and complexity. From these digital twins, it generates reinforcement learning environments with expert-level, multi-step, multi-tool tasks calibrated against the latest frontier models, designed to expose model weaknesses rather than confirm strengths. The company serves frontier AI labs, enterprise AI teams evaluating agents before production, and AI startups that want evaluation infrastructure without building data pipelines themselves.