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Effective URL: https://www.vijil.ai/
Submission: On July 20 via automatic, source certstream-suspicious — Scanned from US
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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.
Join waitlist
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.
Learn more
LLMS BUILT WITH
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.
Join the waitlist
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
SCORE
Evaluate your AI agent in minutes
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