Mixedbread provides an API that transforms documents of any format—PDFs, images, code, audio, video—into searchable, structured content using its proprietary multimodal retrieval models. The service delivers sub‑200 ms query responses and supports multilingual, multimodal search for enterprise, e‑commerce, and context‑engineering applications. It offers scalable, pay‑as‑you‑go pricing with options for regional deployment, SOC2 and ISO 27001 compliance, and on‑premise installations.
Funding
$5.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many AI applications require custom-built search and retrieval systems, which can be complex and time-consuming to develop from scratch. Existing solutions often lack the flexibility to integrate with diverse data sources and adapt to specific use cases, leading to inefficient workflows.
Solution
Mixedbread AI provides a comprehensive AI search stack that simplifies the development of production-ready AI search and Retrieval-Augmented Generation (RAG) applications. The platform offers a suite of tools, including vector stores, embedding and reranking models, and a document parser, enabling developers to transform raw data into intelligent search experiences. Mixedbread's models outperform closed-source alternatives while remaining open-source and cost-effective. The platform integrates with various data sources and offers enterprise-grade security and compliance, allowing users to deploy AI search solutions anywhere, whether on-premises, in a VPC, or in the cloud.
Target Audience
Mixedbread AI targets developers and organizations building AI agents, chatbots, and knowledge systems who need a complete AI search stack with open-source models and enterprise-grade deployment options.
Features
- Vector stores that facilitate building production search engines with multimodal search capabilities across 100+ languages
- Embedding and reranking models that outperform OpenAI in semantic search and RAG applications
- Document parser to extract text, tables, and layouts from PDFs, images, and complex documents
- REST APIs for deploying multimodal semantic search and RAG
- Enterprise deployment options with SOC 2, HIPAA, and GDPR compliance
- Python and TypeScript SDKs for integration
- Models trained with reinforcement learning for improved reasoning and accuracy
- Support for long contexts up to 8k tokens (32k compatible)