Collinear provides a Simulation Lab that creates thousands of sandboxed, high‑fidelity scenarios populated with real‑world entities, APIs, and end‑to‑end workflows. AI teams can run their conversational agents or automation bots in these environments, capture failures, and automatically generate training data to improve response quality, reduce production risk, and accelerate resolution times.
Funding
Funding not disclosed
Founders
Product
Problem
The real world is messy, and AI agents often encounter users, tools, and multi‑turn workflows they have never experienced during development. Traditional static evaluations do not expose agents to this complexity and fail to generate the data needed to correct shortcomings before deployment.
Solution
Collinear offers a Simulation Lab that builds thousands of sandboxed scenarios populated with real‑world entities, software tools, and end‑to‑end workflows. Agents interact with these environments, making mistakes that are captured and turned into actionable training data. By iterating within the lab, teams can improve response quality, reduce production risk, and accelerate resolution times for customers. The platform also provides measurable performance metrics, showing significant gains in AI‑generated responses across enterprise deployments.
Target Audience
Primary customers are AI product teams and data science groups in enterprises that develop conversational agents, virtual assistants, or workflow‑automation bots.
Features
- Automated generation of high‑fidelity simulation worlds that mirror enterprise and consumer workflows
- Integration of real product APIs and tools within sandboxed scenarios for realistic agent interactions
- Continuous failure capture and conversion of error cases into training data for model refinement
- Quantitative reporting on response quality improvements and resolution speed gains
- Scalable token generation (500 B+ tokens) to support frontier model training and evaluation