Adam Hill
Adam is the Interim Director of Data Science at ComplyAdvantage, where he leads a brilliant team tackling financial crime with advanced analytics, large-scale systems, and the latest in generative and agentic AI.
Before that, he spent eight years in the smart cities space at HAL24K, helping governments and infrastructure providers make better decisions with their data. Along the way, he built and led a team of ten data scientists, and helped launch four spin-out ventures—proving that good data science can move the dial in the real world.
A recovering astrophysicist, Adam spent a decade analysing data from space telescopes in search of new cosmic phenomena. He’s since redirected that curiosity toward Earth-based problems.
Adam is an active member of the PyData community, the founder of PyData Southampton, and a long-time volunteer with DataKind UK, supporting charities and NGOs with pro-bono data science.
Sessions
LLMs are magical—until they aren’t. Extracting adverse media entities might sound straightforward, but throw in hallucinations, inconsistent outputs, and skyrocketing API costs, and suddenly, that sleek prototype turns into a production nightmare.
Our adverse media pipeline monitors over 1 million articles a day, sifting through vast amounts of news to identify reports of crimes linked to financial bad actors, money laundering, and other risks. Thanks to GenAI and LLMs, we can tackle this problem in new ways—but deploying these models at scale comes with its own set of challenges: ensuring accuracy, controlling costs, and staying compliant in highly regulated industries.
In this talk, we’ll take you inside our journey to production, exploring the real-world challenges we faced through the lens of key personas: Cautious Claire, the compliance officer who doesn’t trust black-box AI; Magic Mike, the sales lead who thinks LLMs can do anything; Just-Fine-Tune Jenny, the PM convinced fine-tuning will solve everything; Reinventing Ryan, the engineer reinventing the wheel; and Paranoid Pete, the security lead fearing data leaks.
Expect practical insights, cautionary tales, and real-world lessons on making LLMs reliable, scalable, and production-ready. If you've ever wondered why your pipeline works perfectly in a Jupyter notebook but falls apart in production, this talk is for you.