Startup ZeroEntropy joins a growing wave of infrastructure companies hoping to use retrieval-augmented generation (RAG) to power search for the next generation of AI agents. ZeroEntropy offers an API that manages ingestion, indexing, re-ranking, and evaluation. What that means is that — unlike a search product for enterprise employees like Glean — ZeroEntropy is strictly a developer tool. It quickly grabs data, even across messy internal documents. Houir Alami likens her startup to a “Supabase for search,” referring to the popular open source database that automates much of the database management. At its core is its proprietary re-ranker called ze-rank-1, which the company claims currently outperforms similar models from Cohere and Salesforce on both public and private retrieval benchmarks. It makes sure that when an AI system looks for answers in a knowledge base, it grabs the most relevant information first. “Right now, most teams are either stitching together existing tools from the market or dumping their entire knowledge base into an LLM’s context window. The first approach is time-consuming to build and maintain,” CEO Ghita Houir Alami said. “The second approach can cause compounding errors. We’re building a developer-first search infrastructure — think of it like a Supabase for search — designed to make deploying accurate, fast retrieval systems easy and efficient.”