Ooak Data provides an applied AI research platform that converts authentic, multimodal enterprise data into privacy‑preserving digital twins and reinforcement‑learning environments. Their automated pipeline ingests sources such as Slack, Gmail, Notion, Jira, SharePoint, and Teams, anonymizes sensitive information while retaining structural relationships, and creates high‑fidelity, multi‑tool workflow simulations for training and evaluating autonomous agents.
Funding
Funding not disclosed
Founders
Product
Problem
Developing AI agents that can operate effectively within complex enterprise workflows is hindered by a shortage of realistic training and evaluation environments. Existing benchmarks often rely on synthetic or text‑only data, which fails to capture the multimodal documents, communications, and tool interactions that define real company operations.
Solution
Ooak Data offers an applied AI research platform that transforms authentic, multimodal company data into reinforcement‑learning (RL) environments. Their automated pipeline anonymizes sensitive information while preserving the structural relationships among documents, emails, chat messages, project‑management records, and organizational charts, creating high‑fidelity digital twins of enterprise workflows. These digital twins serve as frontier‑calibrated RL environments where autonomous agents can be trained and evaluated on multi‑step, multi‑tool tasks that mirror real‑world business processes. By providing a realistic sandbox, Ooak enables AI teams to assess agent performance, identify gaps, and iterate before deploying solutions in production settings.
Target Audience
Primary customers are frontier AI research labs and enterprise AI teams that need realistic, privacy‑preserving environments to develop and test autonomous agents for complex business workflows.
Features
- Automated ingestion of diverse enterprise data sources (e.g., Slack, Gmail, Notion, Jira, SharePoint, Teams) to capture full organizational context
- Multimodal anonymization that removes names, dates, and proprietary content while retaining structural fidelity and inter‑document relationships
- Generation of digital twins that reflect real‑world workflow complexity, enabling multi‑step, multi‑tool task simulations
- RL environment creation calibrated for frontier AI research, supporting expert‑level agent evaluation beyond single‑turn Q&A
- Compatibility with standard reinforcement‑learning frameworks, allowing seamless integration of custom agents and training pipelines