Intermediate

Enterprise RAG Search

Deploy enterprise RAG search across documents, presentations, reports, and multimedia content. Get AI-generated answers grounded in your organization data.

Who It's For

Financial services firms, consulting organizations, legal teams, and enterprise knowledge workers who need to synthesize information across thousands of internal documents, reports, and presentations

Problem Solved

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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One-click templatesStart from a templateEach template deploys a working search app on your own files.Browse templates

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.

Multimodal RAG

LLMs that cite real clips, frames, and documents

Document Intelligence Search

Search through PDFs with OCR and semantic retrieval

PDF Data Extraction Pipeline

Convert unstructured PDFs into structured data

Feature Extractors Used

document graph extractor

Text Embedding

Extract semantic embeddings from documents, transcripts and text content

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

Build this in the docs

The exact stages and extractors this use case runs on, with API reference and worked examples.

Multi-stage retrieval deep diveCompose the retrieval half of a RAG pipeline stage by stage.PermissionsKeep retrieval inside what each user is allowed to see.

Deploy Enterprise RAG Search

Clone the enterprise RAG pipeline and connect your document repositories for AI-powered question answering.

Estimated setup: 2 hours

Run this on your own data, from $25/mo Book a demo

Frequently Asked Questions

How does enterprise RAG search handle financial tables and charts in PDFs?

The document graph extractor preserves table structures, cell relationships, and header mappings when processing PDFs. Financial tables are indexed with their structure intact, so queries like "Q3 revenue by segment" return the actual table data with row and column context, not just the surrounding text.

Can RAG search provide source citations for every answer?

Yes. Every answer includes citations pointing to the specific document, page number, and passage from which the information was derived. Users can click through to verify the source, ensuring trust and compliance with internal audit requirements.

How is enterprise RAG search different from ChatGPT or general AI assistants?

Enterprise RAG search is grounded exclusively in your organization data, not general internet knowledge. It cannot hallucinate information that does not exist in your documents because every claim must be backed by a retrieved source. This is critical for regulated industries where accuracy and auditability matter.

What enterprise data sources can be connected?

Mixpeek ingests from S3-compatible storage, Azure Blob, Google Cloud Storage, and any system that can export documents. PDFs, Word documents, PowerPoint presentations, Excel spreadsheets, and scanned documents are all processed through format-appropriate extractors.

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Ready to Implement This Use Case?

Our team can help you get started with Enterprise RAG Search in your organization.

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