AI models keep getting better, but most AI systems still fail in production. Why?In this episode of High Bit, Brett Gibson sits down with Ghita Houir Alami, cofounder and CEO of ZeroEntropy, to break down the real bottleneck holding AI agents back: retrieval.Ghita explains why embeddings alone can’t reliably surface the right information, why tools like Slack search feel so frustrating, and how rerankers add a critical second pass that dramatically improves accuracy. She walks through ZeroEntropy’s approach to training rerankers using pairwise comparisons and Elo-style scoring, and why this method generalizes across domains like code, finance, and biology.The conversation goes deep into:Why AI agents fail even when the data exists.How reranking fixes poor ordering from vector search.Why “accuracy” now includes helpful context, not just correct answers.What actually changes when retrieval becomes trustworthy enough to remove humans from the loop.If you’re building AI agents, search systems, customer support bots, or internal knowledge tools, this episode explains what’s breaking today, and what has to change for AI to work reliably at scale.

What changes when retrieval works

What ZeroEntropy builds

Why retrieval became the real problem

Why search fails (Slack included)

Why embeddings fall short

Rerankers: the missing layer

Why rerankers matter most

Pairwise ranking vs scoring

Elo scoring for documents

Fast rerankers via distillation

Why old training methods break

Retrieval for AI agents

Recency, memory, personalization

What reliable retrieval unlocks