ZeroEntropy
| Introduction: | ZeroEntropy provides an adaptive AI retrieval engine API designed to deliver fast, human-like, and accurate search for AI Agents and RAG applications. |
| Recorded in: | 7/11/2025 |
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What is ZeroEntropy?
ZeroEntropy is an adaptive AI retrieval engine that offers an API to solve common frustrations with latency and accuracy in the retrieval layer of AI products. It is designed for developers building AI Agents and RAG applications, providing a sophisticated system that understands context, learns from every query, and delivers accurate, personalized results out-of-the-box. Unlike traditional search engines, ZeroEntropy unifies dense, sparse, and reranked relevance, eliminating the need for complex infrastructure maintenance and constant tuning. It aims to free engineering teams to focus on their core product by handling the heavy lifting of advanced retrieval.
How to use ZeroEntropy
Users can get started with ZeroEntropy through its "Self-Serve" public API by signing up on their dashboard. For those interested in testing new models, agentic search capabilities, and cutting-edge retrieval features, a specific "Test ZeroEntropy Reranker Model" option is available. For enterprises or teams with strict compliance or data residency needs, ZeroEntropy offers "Secure & Custom Deployment" options, including running in a Virtual Private Cloud (VPC) or on-premise, which requires booking a demo to discuss. Pricing is not detailed for the self-serve option, but custom pricing is available for enterprise solutions, and the core interaction involves integrating with their API.
ZeroEntropy's core features
Adaptive AI retrieval engine
Context-aware understanding of data and queries
Self-improving system that learns from every query and interaction
Dynamically chooses optimal retrieval strategies
Delivers fast, human-like, and accurate search results
Provides a single API for cutting-edge retrieval
Automated improvement in accuracy and latency
Eliminates the need for complex infrastructure maintenance (vector DBs, pipelines, rerankers)
Enterprise-grade security with SOC 2 Type II compliance and HIPAA readiness
Optimized for retrieval quality out-of-the-box, combining dense, sparse, and reranked relevance
Use cases of ZeroEntropy
Building and enhancing AI Agents with human-level search capabilities
Developing and optimizing Retrieval Augmented Generation (RAG) applications
Improving the accuracy and reducing latency of search within AI-powered products
Replacing fragmented and complex retrieval layers (e.g., stitching together vector databases, pipelines, and rerankers)
Freeing engineering teams from maintaining retrieval infrastructure to focus on core product innovation
Deploying advanced search solutions in environments with strict compliance or data residency requirements (VPC or on-prem)
Integrating state-of-the-art relevance into applications without manual tuning of search parameters
FAQ from ZeroEntropy
What makes ZeroEntropy different from traditional search engines?
Traditional search uses static keyword or semantic matching. ZeroEntropy is optimized for retrieval quality out of the box — combining dense, sparse, and reranked relevance in a single API. We treat every query as a learning opportunity: • You get state-of-the-art relevance, not a bag-of-words match. • You don’t need to tune BM25 weights, vector thresholds, or rerank configs — we handle that. • You don’t maintain an infra Frankenstein of vector DBs, LLMs, pipelines — we unify it.