Enterprise RAG Search
Deploy enterprise RAG search across documents, presentations, reports, and multimedia content. Get AI-generated answers grounded in your organization data.
Financial services firms, consulting organizations, legal teams, and enterprise knowledge workers who need to synthesize information across thousands of internal documents, reports, and presentations
Enterprise teams spend hours searching across document repositories, SharePoint sites, and email archives to find information buried in PDFs, presentations, and spreadsheets. When information spans multiple documents, analysts must manually locate, read, and synthesize across sources. Critical decisions are delayed because the relevant data exists but cannot be found efficiently.
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Why Mixpeek
The document graph extractor preserves table structures, chart data, and spatial layouts that flat text extraction loses. Multimodal embeddings make charts and diagrams searchable alongside text. Source attribution ensures every AI-generated answer is traceable to specific documents.
Overview
Enterprise RAG search enables knowledge workers to ask natural language questions and receive synthesized answers drawn from across all enterprise documents, with full source attribution. By understanding tables, charts, and document structure, the system surfaces information that keyword search and flat text extraction miss.
Challenges This Solves
Information Silos Across Repositories
Enterprise data lives in SharePoint, shared drives, email, CRM notes, and proprietary systems with no unified search across all sources
Impact: Analysts spend 20-30% of their time on information retrieval across disconnected systems
Structured Data in Unstructured Documents
Critical data points are locked in PDF tables, PowerPoint charts, and Excel reports that standard text extraction cannot parse correctly
Impact: Financial figures, performance metrics, and comparison data in document tables are invisible to search
Multi-Document Synthesis Burden
Answering complex questions requires locating and synthesizing information from 5-10 documents across different departments and time periods
Impact: Research tasks that could take minutes with proper tooling consume hours or days of analyst time
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Feature Extractors Used
document graph extractor
text extractor
multimodal extractor
Retriever Stages Used
feature-search
rerank
Rerank documents using cross-encoder models for accurate relevance
rag-prepare
attribute-filter
Expected Outcomes
80% faster from question to answer
Information retrieval time
Automated multi-source answers with citations
Cross-document synthesis
Structured data searchable for the first time
Table and chart data accessibility
25% more time on analysis vs. information gathering
Analyst productivity
Deploy Enterprise RAG Search
Clone the enterprise RAG pipeline and connect your document repositories for AI-powered question answering.
Frequently Asked Questions
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Ready to Implement This Use Case?
Our team can help you get started with Enterprise RAG Search in your organization.