AI for Investors: A Builder's Playbook for Enterprise LLMs

I'll share hard-won, practical musings from my startup's journey building production-grade LLMs for institutional investors. We'll move beyond the hype and the "demo trap" to discuss the real-world engineering challenges. I'll cover the core tensions between API costs and performance, and the critical, complex cycle of aligning AI outputs with non-technical stakeholders in legal, brand, and compliance. We'll dive into our key solutions for the biggest production hurdles: managing messy, real-world data entropy (hint: it requires more than just vector databases), controlling model non-determinism to ensure reliable and citable outputs crucial for finance, reducing the high cost of iteration, and managing non technical stakeholder expectations.