Verax AI provides a Control Center that enables organizations to monitor and manage large language models (LLMs) in real-time, automatically detecting and correcting issues such as hallucinations and data leakage. This platform enhances operational safety and compliance by ensuring verified AI outputs while mitigating risks associated with unpredictable LLM behavior in production environments.
Funding
$8.3M 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.
Founders
Product
Problem
Large language models (LLMs) in production environments exhibit unpredictable behaviors, leading to issues such as hallucinations, biases, and data leakage. Traditional monitoring tools often fail to address these LLM-specific challenges, leaving organizations vulnerable to financial, regulatory, and reputational risks. Delayed detection and remediation of these issues can result in significant damage.
Solution
Verax AI offers a Control Center that provides real-time visibility and management of LLMs, enabling organizations to automatically detect and correct issues like hallucinations, off-topic responses, and data leakage. The platform delivers in-depth behavioral insights, allowing for precise tailoring of AI services and continuous improvement by correcting biases, inaccuracies, and data drifts. By anticipating and preventing undesired LLM responses, Verax helps ensure verified, responsible, and safe AI, mitigating risks and maintaining compliance. The platform also prevents data leakage to and from LLMs, protecting sensitive information and controlling access to personally identifiable information (PII).
Target Audience
Verax AI targets IT leaders seeking real-time visibility and alerts for their LLMs, data science leaders aiming to optimize LLMs in production, and innovation leaders looking to successfully implement LLMs in production workloads.
Features
- Real-time detection and remediation of false, hallucinatory, and biased responses
- Trend analysis for understanding user behavior and tailoring AI services
- Risk management tools to mitigate real-time risks
- Data leakage prevention to protect against financial, regulatory, and brand damage
- Customizable analysis for personalized interactions
- Ability to limit stored Personally Identifiable Information (PII) to authorized access only
- Automatic correction of issues to minimize LLM modifications and reduce manual work