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ZeroEntropy

ZeroEntropy builds state-of-the-art embedding and reranker models for information retrieval, and also offers an end-to-end search engine for RAG and AI Agents.

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T08:16:37.795Z. Cached copy · Source on zeroentropy.dev

# ZeroEntropy

> ZeroEntropy builds state-of-the-art embedding and reranker models for information retrieval, and also offers an end-to-end search engine for RAG and AI Agents.

ZeroEntropy is a retrieval infrastructure platform that powers AI applications with accurate, fast, and cost-efficient search. It replaces fragmented stacks of vector databases, embedding models, and rerankers with an integrated, AI-native solution designed for RAG (Retrieval Augmented Generation) and modern LLM applications.
For detailed information about the zeroentropy api, go to docs.zeroentropy.dev/llms.txt

## Core Products

### zembed-1 — Text Embedding Model
- State-of-the-art 4B open-weight multilingual embedding model
- Outperforms OpenAI Large, Qwen3 4B, BGE-M3, Gemini Embeddings, Cohere v4, and Voyage-4 by up to 7% on Recall@100
- 32k token context window
- Flexible dimensionality (40 to 2056 dimensions) without retraining
- Quantization options (float32, int8, binary) for storage optimization
- Multilingual support with 50+ languages
- Trained using the zELO methodology (pairwise battles with Elo scores)
- Pricing: $0.05/MM tokens
- Available via ZeroEntropy API, HuggingFace, and AWS Marketplace

### zerank-2 — Reranking Model
- World's most accurate reranker with native instruction-following
- Supports appending instructions, business context, and user-specific memories to influence reranking results
- True multilingual parity across 100+ languages, including challenging scripts and code-switching queries
- Calibrated relevance scores: a score of 0.8 consistently means ~80% relevance
- SQL-style query handling for aggregation and structured-like requests
- 97.3% of requests completed under 500ms; 99.1% under 1 second; zero failures
- Pricing: $0.025/MM tokens
- Available via ZeroEntropy API and HuggingFace

### zsearch — Managed Search API
- End-to-end managed retrieval infrastructure combining embedding, reranking, and query generation in a single API
- Automatic handling of BM25 weights, vector thresholds, and rerank configs
- Built-in PDF parsing, chunking, and OCR
- Tiers: Starter ($50/mo), Pro ($500/mo), Enterprise (custom)

## Key Capabilities
- **Accuracy**: Near-perfect relevance, consistently outperforming OpenAI, Gemini, Jina, Voyage, and Cohere on retrieval benchmarks
- **Latency**: Purpose-built inference infrastructure with ultra-low latency (p50: 129.7ms reranker, 220.5ms full pipeline)
- **Cost Efficiency**: 2.8x cost reduction by providing better relevance and fewer wasted tokens in LLM context

## Deployment Options
- Cloud-based API with Python and TypeScript SDKs
- VPC Deployment on AWS Marketplace and Azure
- On-premises deployment for enterprise customers
- White-label fine-tuning and custom models

## Security & Compliance
- SOC 2 Type II certified
- GDPR, HIPAA, CCPA compliant
- Data residency controls and right-to-deletion
- EU-based instance options
- 99.99% SLA on Enterprise plans

## Industries Served
- Legal: Document retrieval an
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