Veris AI builds high-fidelity simulation environments that replicate enterprise technology stacks for training and testing AI agents. This platform enables agents to learn from thousands of real-world scenarios, ensuring robustness and continuous improvement through reinforcement learning before and after deployment.
Funding
Funding not disclosed
Founders
Product
Problem
Training enterprise AI agents effectively for complex, real-world tasks is challenging due to the lack of realistic, safe environments. This gap hinders the ability to thoroughly test, stress-test, and fine-tune agents before deployment, leading to potential performance issues and operational risks.
Solution
Veris AI provides a platform for creating high-fidelity simulation environments that precisely mirror an enterprise's existing technology stack, including all tools and user interactions. These dedicated sandboxes allow AI agents to learn through experience, performing common and edge-case scenarios thousands of times. The platform captures detailed performance data, which is then used with reinforcement learning and other optimization techniques to fine-tune the agent's model. This iterative process ensures agents are robust, confident, and continuously improving before and after deployment into production environments.
Target Audience
The primary customers are enterprises and developers building AI agents for operational tasks across various sectors, including manufacturing, fintech, and customer support, who require robust training and validation infrastructure.
Features
- High-fidelity simulation environments that replicate an enterprise's specific technology stack, including applications, APIs, and user interfaces.
- Automated generation of common and edge-case scenarios for agent training and stress-testing.
- Comprehensive data logging of agent performance against user-defined metrics.
- Integrated reinforcement learning (RL) and optimization engine for post-training model fine-tuning.
- Ability to mirror production environments to minimize the simulation-to-production gap.
- Support for training agents on tasks involving complex interactions like email threads and API calls.
- Continuous environment updates based on observed real-world agent performance.