UK Poindexter Labs Raises £2M To Improve Training Data For AI Models

The round was led by Episode 1 and First Cheque, with participation from Venture Friends partners and several angel investors. Notably, the round also included contributions from Poindexter’s own network of expert collaborators, including mathematicians and scientists working on the platform.

As AI systems advance demand is rising for high-quality training data produced by domain experts. However much of today’s data infrastructure is still designed for simpler tasks such as image tagging and text classification, rather than complex reasoning.

Poindexter Labs is building a platform that enables experts to generate and validate training data involving deep reasoning and specialist knowledge across fields such as mathematics, science, law, medicine, finance, and engineering.

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The company’s proprietary platform, currently in beta, supports both its own data production services and external organisations building AI systems. It provides collaborative workflows that help subject-matter experts produce higher-quality, structured training data at scale.

With the new funding, Poindexter Labs plans to expand its team, strengthen relationships with frontier AI labs, and roll out its platform to more enterprise and public-sector customers developing advanced AI systems.

According to Jocelyn D'Arcy, Founder and CEO of Poindexter Labs, many of the workflows still used to generate AI training data prioritise scale over collaborative knowledge creation.

As a result, a huge chunk of training data is discarded not because it is wrong, but because adversarial review processes actively incentivise discarding tasks rather than improving them. This is a workflow problem, and we built Poindexter the way academics build knowledge: collaboratively, transparently, with peer review at every step.

About Poindexter Labs

Founded in 2025, Poindexter Labs builds high-quality training data for advanced AI models. The company creates collaborative, expert-led workflows to generate and validate complex knowledge across domains. Its platform is designed to improve the quality of AI training data and support breakthroughs in frontier AI development through more reliable information.