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ZeroEntropy gives you full control over search quality — from the models that power retrieval and reranking, to the architecture that runs complete search pipelines.

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Models

ZeroEntropy offers state-of-the-art models for retrieval and reranking:
  1. zerank-1 and zerank-1-small
    Cross-encoder rerankers that can dramatically improve the ordering of your search results.
    They work on top of any vector or hybrid retriever, boosting precision by learning subtle semantic distinctions across complex domains — from legal and financial documents to code and scientific text.
  2. zembed-1 (early preview)
    A high-quality embedding model optimized for retrieval across multilingual and technical datasets.
    Available upon request for early access.
Learn more about model specs, performance, and examples in the Models section →

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End-to-End Search Engine

Beyond individual models, ZeroEntropy provides an end-to-end search engine that integrates retrieval, reranking, and query orchestration into a single unified API.
You can build custom indexes, control document granularity, and query at the level of collections, documents, pages, or snippets. See the Architecture section → for a detailed breakdown of how all components connect.

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Data Concepts

  1. Collections: Independent datastores for your documents — ideal for multi-tenant or multi-dataset setups.
  2. Documents: Core indexing units. Each document can include metadata for document-level filtering.
  3. Pages: Ordered segments within a document, preserving contextual flow (e.g., PDF pages or sequential messages in a conversation).

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Query Modes

  1. Top-K Documents — retrieve the most relevant documents for a query.
  2. Top-K Pages — retrieve the most relevant pages within documents.
  3. Top-K Snippets — fine-grained retrieval for short, high-precision results (≈200–2000 characters), with built-in reranking

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Examples

  • Files: PDFs or .docx uploads are automatically parsed into pages.
  • Conversations: Treat each Slack channel as a document and each message as a page to preserve message order.
  • CSVs: Each row (e.g., SKU) can be indexed as a document for structured retrieval.
By understanding these concepts, you can combine ZeroEntropy’s models and search architecture to build the most accurate and customizable retrieval systems available.
Next: explore the API Reference →