Can you trust your autonomous agent?

Vijil helps organizations build and operate autonomous agents that humans can trust

AI agents that you can trust

Vijil private cloud services harden models during development, defend models during operation, and evaluate reliability, security, and safety continuously.

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Enterprises cannot trust large language models today.

Enterprises cannot deploy generative AI agents in production today because they cannot trust LLMs to behave reliably in the real world. LLMs are prone to errors, easy to attack, and slow to recover. Even if they were originally aligned to be honest and helpful, they can be easily compromised. Broken free from guardrails, they can diverge from developer goals, degrade user experience, and damage enterprise reputation and revenue.

Truth

Consequences

Reliability under Pressure

LLMs are unreliable because they fail to generalize beyond training data and they generate unverified predictions ("hallucinations").

Vulnerability to Attacks

LLMs are vulnerable to an unbounded set of attacks via jail breaks, prompt injections, data poisoning, and model tampering by malicious actors.

Propensity for Harms

LLMs have a propensity to generate toxic content, reinforce malicious stereotypes, produce unethical responses, and lead to unfair decisions.

shortens time-to-trust™

Vijil red-team and blue-team cloud services harden models during fine-tuning, observe and defend agents and RAG applications during operation, and evaluate generative AI system reliability, security, and safety continuously.

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LLMs built with

trusted by design,
tested with rigor.

trusted by design, tested with rigor.

trusted by design,
tested with rigor.

For enterprises that want to build and operate chatbots, virtual assistants, co-pilots, and autopilots that they can trust in production, Vijil provides private cloud services for AI engineers to measure, improve, and maintain trust in agents based on open, safe, and secure models.

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Harden LLMs during development

Reduce vulnerability to attacks and mitigate technical risks before you deploy models

Defend LLMs during operation

Detect attacks and limit blast radius of models in production

Evaluate LLMs for Trust continuously

Test the system holistically under benign and hostile conditions to measure reliability, security, and safety with rigorous standards