White Circle, an enterprise AI monitoring and control platform, has closed an $11m seed funding round backed by some of the most influential figures in the artificial intelligence industry.
The round drew investment from a constellation of AI luminaries, including Romain Huet of OpenAI, Dirk Kingma — formerly of OpenAI and now at Anthropic — Guillaume Lample of Mistral, Thomas Wolf of Hugging Face, Olivier Pomel of Datadog, François Chollet of Keras, Mehdi Ghissassi from ex-DeepMind, Paige Bailey of DeepMind, and David Cramer of Sentry. The proceeds will fund accelerated product development, team expansion across the US, UK and Europe, and growth of White Circle’s global client base.
The company was established by engineer Denis Shilov, whose profile rose dramatically in 2024 after he demonstrated that a single prompt could bypass the safety measures of every leading AI model — enabling access to instructions for producing drugs and weapons, and extracting content from systems including ChatGPT and Claude that had been explicitly built to block such outputs.
The post accumulated 1.4m views and attracted the attention of Anthropic, OpenAI, and Hugging Face, resulting in Shilov being invited to join Anthropic’s bug bounty programme. The broader demand that followed led to the creation of White Circle.
White Circle positions itself as the only comprehensive, enterprise-grade platform capable of testing, protecting, monitoring and continuously improving AI systems in real time.
Its proprietary models oversee AI inputs and outputs simultaneously, applying each organisation’s bespoke policies to detect harmful content, catch hallucinations, prevent prompt injection attacks, flag model drift, and identify abusive users. All functionality is accessible through a single API. The platform supports 150 languages and is both SOC 2 Type I and Type II certified and HIPAA compliant.
In practice, the platform can detect when a FinTech model exposes sensitive customer data, stop an AI agent from carrying out destructive commands such as deleting files, and identify rising user frustration when a model begins to underperform. Organisations can define their own enforcement policies — including automated actions such as rate limiting or banning bad actors. The system also improves continuously by learning from labelled user feedback, adapting more precisely to each organisation’s specific use case. The platform’s design is led by head of design Elena Iumagulova.
White Circle’s research efforts have further shaped industry understanding of model risk. In May 2025, it published CircleGuardBench, a benchmark designed to evaluate how effectively AI moderation models perform in real-world conditions, allowing anyone to assess model performance directly. The company also released its KillBench study, the product of more than a million experiments across 15 AI models from OpenAI, Google, Anthropic and xAI.
The research revealed hidden biases in how these models approach decisions involving human lives — with all models displaying preferences along lines of nationality, religion, body type and even phone brand. The study further found that structured output — the standard method for integrating AI into production systems — caused refusal rates to drop and amplified those biases.
White Circle founder and CEO Denis Shilov said, “AI is moving faster than our ability to guide it. We already trust it with decisions that touch millions of people – from hiring to healthcare, finance and security – and with the rise of vibe coding, anyone can ship an AI product without knowing what that model is actually doing once it’s live. Until now there’s not been a platform purpose-built to monitor AI’s behaviour, catch it when it goes wrong, or shape how it acts. With White Circle, we’re finally giving companies everything they need to hold their AI accountable and optimise their models in a single place, without sacrificing security, compliance or risk.”
White Circle head of design Elena Iumagulova said, “We wanted any team, technical or not, to be able to see exactly what their AI is doing in one place – whether they’re getting visibility into their models for the first time or running AI at scale and need to catch problems fast. Everything from real-time flagging to analytics and optimisation lives in one place, connected via a single API.”
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