Vijil provides AI developers with red-team and blue-team cloud services to enhance the reliability, security, and safety of autonomous agents during both development and operation. The platform addresses the lack of trust in large language models by continuously evaluating their performance under various conditions to mitigate vulnerabilities and ensure consistent behavior in production environments.
Funding
$6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
MFFounders
Product
Problem
Enterprises are hesitant to deploy generative AI agents due to concerns about their reliability, security, and safety in real-world scenarios. Large language models (LLMs) are prone to errors, vulnerable to attacks, and can exhibit inconsistent behavior, leading to potential damage to user experience and enterprise reputation.
Solution
Vijil offers AI developers red-team and blue-team cloud services designed to enhance the reliability, security, and safety of autonomous agents throughout their lifecycle. The platform hardens models during fine-tuning, observes and defends agents and Retrieval-Augmented Generation (RAG) applications during operation, and continuously evaluates generative AI system reliability, security, and safety. By providing tools to measure, improve, and maintain trust in agents based on open, safe, and secure models, Vijil shortens the time-to-trust for enterprises deploying AI.
Target Audience
Vijil's primary customers are AI engineers and enterprises building and operating chatbots, virtual assistants, co-pilots, and autopilots who require trustworthy AI agents in production.
Features
- Red-team services to identify vulnerabilities and attack vectors in LLMs.
- Blue-team services to detect attacks and limit the blast radius of models in production.
- Continuous evaluation of AI agents under benign and hostile conditions.
- Tools to measure reliability, security, and safety with rigorous standards.
- Hardening of LLMs during development to reduce vulnerability to attacks and mitigate technical risks.