Can you trust your autonomous agent?

Vijil is on a mission to help organizations build autonomous agents that humans can trust.

To build a trustworthy agent, you must first build trustworthy AI models. To do that, you must be able to inspect, customize, and control the models. This is easier to do when the models are open-source than when they are closed.

Vijil helps organizations that are customizing open-source AI models for new domains, tasks, and data. For developers augmenting or fine-tuning large language models, Vijil provides tools to harden models during development, defend models during operation, and verify trust continuously.

An autonomous agent
impossible object

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, open to threats, easy to breach, and slow to recover. Even if they were originally designed for honest and helpful use, they can be misused to produce hallucinations, toxic content, inexplicable outputs, and unfair outcomes.

Vulnerability to Attacks

LLMs are vulnerable to an unbounded set of attacks including prompt injection, model theft, data theft, model evasion, and data poisoning.

Propensity for Harms

LLMs have a propensity to produce ungrounded predictions, generate toxic content, reinforce stereotypes, and lead to unfair decisions.

To build trustworthy autonomous agents

Build Trustworthy  LLMs

Vijil offers metrics and mechanisms for defense in depth, hardening models from the inside as well as bolting guardrails on the outside.

Harden LLMs during development

Reduce vulnerability to attacks and mitigate propensity for harm before you deploy

Defend LLMs during operation

Detect attacks and limit harms after you deploy

Evaluate LLMs for Trust continuously

Use benchmarks backed by academic standards

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Trustworthy AI

Meet the Team

On the mission to build trustworthy AI

Vin Sharma
Vin Sharma
Co-Founder / CEO

Vin has over 25 years of experience at Hewlett-Packard, Intel, and AWS building trust into open source platforms for OS (Linux), virtualization (KVM), cloud (OpenStack), data (Hadoop), analytics (Spark), and deep learning (TensorFlow, PyTorch).

Subho Majumdar
Dr. Subhabrata Majumdar
Co-Founder / Head of AI

Subho co-authored the (O'Reilly) book Practicing Trustworthy Machine Learning and established responsible AI practices at Twitch and Splunk. As President of the AI Risk & Vulnerability Alliance non-profit, he leads the AVID open source project.

Zdravko Pantic
Zdravko Pantic
Co-Founder / Head of Engineering

Zdravko led the team that built the Amazon SageMaker deep learning  platform with AWS-optimized libraries for distributed training in TensorFlow and PyTorch. Starting with BERT, LLMs including BloombergGPT, StableDiffusion, and Falcon were trained on this platform.

Leon Derczynski
Dr. Leon Derczynski
Advisor / Research

Leon is Associate Professor at ITU Copenhagen, founder of the open source LLM vulnerability scanner Garak, and contributor to OWASP Top 10 for LLM.


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