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Enterprise Layer Intelligence
Categories/AI Orchestration & MLOps/Mixedbread
Mixedbread

Mixedbread

Founded 2023

Index and retrieve company documents to supply precise evidence for AI agents

Pricing model

Free Tier, Paid

You can use Mixedbread to turn your company documents, videos, code, and communication channels into structured search indexes for AI agents. Instead of running separate embedding pipelines, vector databases, and rerankers, you connect files or services like Google Drive and Slack directly to the service. It handles optical character recognition, audio transcription, chunking, and multi-vector indexing automatically. When your agents need context, Mixedbread searches across your data using semantic search, grep, or agentic retrieval loops, returning exact source passages with citations while consuming fewer tokens.

What Mixedbread does

Upload and parse PDF documents with automated text extractionIndex enterprise repositories and codebases via APITranscribe audio and video files for semantic searchingPerform agentic multi-step context retrieval with automated tool callsRerank vector search candidate hits to prioritize relevant passagesTrace search query execution steps and review bounding box sourcesMultimodal parsing and indexing for text, images, audio, video, and code across 100+ languagesNative multi-vector storage engine designed to prevent recall lossAgentic search loop that carries out multi-step queries, inspection, and greppingListwise reranker to re-order candidate passages by relevanceAuditable search traces with page numbers, bounding boxes, and source filesDedicated Python and TypeScript software development kits alongside MCP support

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
StarterFree
Scale$20 / mo

Frequently asked

SlackSlackGoogle DriveGoogle DriveOpenCodeOpenCodeHugging FaceHugging Face

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Mixedbread, vector search, embeddings, reranking, agentic search, document retrieval, multi-vector embeddings, RAG, Toast 1, Silo database, Wholembed, semantic search, AI context, multimodal retrieval, OCR indexing, code search