Enterprise-Grade RAG That Actually Works.

The Enterprise RAG platform that offers precise retrieval and semantic search
across modalities and 100+ languages,
ready out of the box.

Explore Our Models

Integrates with Google Drive, SharePoint, Notion, Slack, and more

Trusted research with over 50M+ downloads on Hugging Face

Models Used by the Best

Transform Enterprise Knowledge into Immediate Insights

Stop struggling with data silos. Deploy our platform to unlock the hidden value in your unstructured content and deliver answers, not just search results.

Search Engine

Accelerate decision-making with our context-aware search that delivers precise answers from documents, images, videos, and code across 100+ languages with 90% less irrelevant results.

Q4 2023 financial statements

ESC

3 Results found in multiple formats

2023 Q4 Earnings Report - Acme Inc.PDF, English, 42 pages

2023年第4季度财务报告 - Acme Inc.Video, Chinese, 10 minutes

Q4 2023 Consolidated Financials - Acme Inc.XLSX, English, 4 sheets

Enterprise-Tuned Models

Achieve 40-60% higher accuracy with our domain-adapted Embedding and Reranking models that understand your specific terminology and content – unlocking insights hidden from general-purpose models.

Reranking Model PerformanceBEIR (Relative)

mxbai-rerank-large-v2100%

cohere-rerank-3.596.3%

bge-reranker-v2-gemma96.3%

voyage-rerank-294.9%

Document Intelligence

Convert unstructured content into structured, actionable data with industry-leading 95%+ extraction accuracy – automating months of manual processing in minutes.

FINANCIAL Zz4QT9pWm

Secure & Compliant Deployment

Deploy with enterprise-grade security that meets , , and requirements. Available as on-premises, VPC, or cloud deployments that scale to millions of documents while maintaining complete data sovereignty and control.

System StatusAll Systems Operational

Vector Stores

99.99% Uptime

Embedding Models

99.99% Uptime

Document Parsing

99.99% Uptime

Reranking Models

99.99% Uptime

Industry-Leading Open Source Models

Our models outperform closed-source alternatives while staying accessible to everyone through Hugging Face.

50M+

Total Model Downloads on
Hugging Face

100+

Languages supported by
mxbai-rerank-v2

40x

Cost Savings with BQ and
mxbai-embed-large-v1

3.5M+

Monthly Model Downloads on
Hugging Face

From Zero to Production in Minutes

Simple, powerful APIs that developers love. Get a complete RAG pipeline or search solution into production with 5x less code and infrastructure. Without sacrificing control or performance.

Documentation API Reference

Create a Vector Store

POST/v1/vector-stores

1from mixedbread import Mixedbread
2
3mxbai = Mixedbread(api_key="YOUR_API_KEY")
4
5res = mxbai.vector_stores.create(
6  name='My Documents',
7  metadata={'project_id': 'proj_123'},
8)
9
10print(res.id)
11print(res.status)