ZeroEntropy

What ZeroEntropy does

ZeroEntropy develops state-of-the-art AI models for information retrieval, including rerankers, embedders, and end-to-end retrieval pipelines. These models are designed to be light-weight, blazing fast, and accurate, outperforming generalist models in production AI systems. The technology powers intelligent search systems that can index, search, and retrieve documents with exceptional precision, offering a retrieval backbone for various AI applications from chatbots to AI agents.

Products

• zerank-2A flagship reranker model for dramatically more accurate retrieval with one line of code. It offers instruction-following capabilities to tailor reranking behavior for specific customer verticals.
• zembed-1A flagship embedding model that outperforms leading embedding models even at lower dimensionality.
• ze on-premOffers ZeroEntropy's state-of-the-art models for licensing and deployment on customer infrastructure, with optional evaluations and fine-tuning.
• Search APIA pay-as-you-go API for retrieval infrastructure, including OCR, indexing, storage, queries, reranking, and embeddings.
• Custom ModelsFine-tuned specialized models for specific stacks, including query rewriting for enterprise APIs, context compression, and bespoke models for production agents.

Key capabilities

• State-of-the-art rerankers, embeddings, and custom models
• Light-weight, blazing fast, and accurate models
• Purpose-built inference infrastructure for low latency
• Open-weight models run on optimized serving stacks
• Model Access: Combine zerank-2 reranker and zembed-1 embeddings
• Document Indexing: Seamlessly add and manage documents
• Advanced Querying: Retrieve relevant documents, pages, or snippets with fine-grained control
• Security: Encrypted API calls, optional on-premises deployment within VPC
• Agentic Retrieval: Actively determines optimal strategy to find information based on query context
• Human-Level Retrieval for Manufacturing
• Retrieval That Understands Medicine
• Retrieval That Thinks Like an Analyst
• Retrieval That Resolves on First Contact

Use cases

• Search and RAG pipelines
• Real-time AI applications and agents at scale
• Query rewriting for enterprise APIs
• Context compression
• Bespoke models for production agents
• Retrieval infrastructure for AI from demos to large-scale products
• Retrieval infrastructure for chatbots to AI Agents
• Maintenance & Troubleshooting in manufacturing
• Quality & Compliance in manufacturing
• Engineering Knowledge in manufacturing
• AI Agents in manufacturing for root-cause analysis, report generation, diagnostics
• AI agents for customer support across chat, email, and phone
• Clinical Decision Support in healthcare
• Medical Research
• Patient Record Retrieval
• Regulatory & Compliance in healthcare
• Financial Research
• Compliance & Regulatory in finance
• Document Analysis in finance
• AI Agents in finance for report generation, due diligence, portfolio analysis
• Ticket Deflection in customer support
• Agent Assist in customer support
• AI Copilots in customer support
• Knowledge Management in customer support

AI approach

ZeroEntropy develops state-of-the-art AI models for information retrieval, including rerankers, embedders, and end-to-end retrieval pipelines. They train specialized, light-weight, blazing fast, and accurate task-specific models (like zembed-1 and zerank-2) for production AI systems, outperforming generalist models in accuracy and latency. Their approach focuses on 'Agentic Retrieval' which mimics human reasoning by dynamically selecting and combining retrieval techniques and improving through feedback. They also offer fine-tuning for custom models.

Tech named: rerankers, embeddings, custom models, zembed-1, zerank-2, MCP server, optimized serving stacks, retrieval pipelines, Agentic Retrieval, dense vector search, keyword-based retrieval, LLM

Industries served

• Technology, Information and Internet
• Manufacturing
• Healthcare
• Finance
• Customer Support

What it says sets it apart

• Specialized models replace generalist alternatives with state-of-the-art accuracy
• Unmatched latency of specialized models (e.g., ~80 ms p90 latency after vs. ~500 ms before)
• Small, focused models run faster than generalist alternatives
• Models consistently outperform leading generalist models across standard benchmarks
• Better specialized models cut cost across the stack (e.g., 2.8x reduction in cost for Assembled)
• Human-level retrieval for domain-specific data (manufacturing, medical, financial, support)
• High accuracy on domain-specific benchmarks (e.g., 95.41% NDCG@10 on TRECCOVID for healthcare)
• Cheaper than API-based models
• Production-validated performance through live traffic experiments
• Instruction-following capabilities for rerankers to tailor behavior without model changes

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.