As generative AI reshapes industries, one of its most important yet invisible challenges is retrieval, the process of fetching the right data with relevant context from messy knowledge bases. Large language models (LLMs) are only as accurate as the information they can retrieve.
That’s where ZeroEntropy wants to make its mark. The San Francisco-based startup, co-founded by CEO Ghita Houir Alami and CTO Nicolas Pipitone, has raised $4.2 million in seed funding to help models retrieve relevant data quickly, accurately, and at scale.
The round was led by Initialized Capital, with participation from Y Combinator, Transpose Platform, 22 Ventures, a16z Scout, and a long list of angels, including operators from OpenAI, Hugging Face, and Front.
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. Competitors range from MongoDB’s VoyageAI to early fellow YC startups like Sid.ai.
“We’ve met a lot of teams building in and around RAG, but Ghita and Nicolas’s models outperform everything we’ve seen,” says Zoe Perret, partner at Initialized Capital. “Retrieval is undeniably a critical unlock in the next frontier of AI, and ZeroEntropy is building it.”
Retrieval-augmented generation (RAG) grabs data from external documents and has become a go-to architecture for AI agents, whether it’s a chatbot surfacing HR policies or a legal assistant citing case law.
Yet the ZeroEntropy founders believe that for many AI apps, this layer is fragile: a cobbled collection of vector databases, keyword search, and re-ranking models. ZeroEntropy offers an API that manages ingestion, indexing, re-ranking, and evaluation.
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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. …