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.
