Toryx offers an open‑source verification layer that attaches to any large language model, automatically generating proof artifacts that document the reasoning behind each output. Its policy engine enforces compliance rules in real time, providing auditable logs and dashboards so regulated enterprises can meet regulatory standards without redesigning their AI pipelines.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations using large language models face unpredictable hallucinations that can produce inaccurate or non‑compliant outputs, making it difficult to meet regulatory standards and to trust AI‑driven decisions.
Solution
Toryx provides an open‑source platform that layers verification and compliance onto existing AI architectures. By capturing the model’s reasoning trace and generating provable evidence for each output, the system turns raw facts into auditable results that can be acted upon. The platform integrates directly with a company’s current model pipelines, requiring no redesign of the underlying AI while ensuring that every response is traceable and meets defined compliance rules. This approach enables organizations to satisfy regulatory requirements and operational controls without sacrificing the flexibility of their AI deployments.
Target Audience
Primary customers are enterprises and regulated industries—such as finance, healthcare, and legal services—that deploy AI models and need to ensure output accuracy and compliance.
Features
- Open‑source verification layer that can be attached to any LLM or generative AI model
- Automatic generation of proof artifacts that document the reasoning path behind each output
- Policy engine for enforcing compliance rules and filtering hallucinated content in real time
- Auditable logs and dashboards that provide transparent evidence for regulatory reporting
- Seamless integration via APIs and SDKs, preserving existing model architecture and performance