What types of documents can LlamaIndex work with?

LlamaParse supports 90+ file types including PDFs (with complex tables and images), Word documents, spreadsheets, web pages, databases, Notion pages, and Confluence wikis. If your data is digital, we can index it.

How accurate are the answers?

LlamaIndex benchmarks at 35% better retrieval accuracy than alternatives. We add source citations so every answer can be verified, and run accuracy benchmarks against known questions to measure and improve performance.

How often is the knowledge updated?

We configure update schedules based on how frequently your documents change: real-time, hourly, daily, or on-demand. Critical documents can trigger immediate re-indexing when modified.

Can the agent handle complex documents with tables and images?

Yes. LlamaParse is enterprise-grade and handles complex layouts that other parsers miss, including multi-column tables, embedded images, and even handwritten notes in scanned documents.

Is my data secure during indexing?

Yes. Documents are processed and stored on secure infrastructure with encryption. Indexes can run on your own servers if needed. We never share your data between clients or use it for other purposes.

Document-Powered Agents With LlamaIndex

We use LlamaIndex to build agents that parse complex documents (tables, images, handwritten notes), index them with advanced retrieval, and answer questions with citations. 48,000+ GitHub stars, 10M+ monthly downloads, used by Salesforce Agentforce.

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We build, deploy, and manage your AI agent. You focus on your business.

What Is LlamaIndex?

LlamaIndex is an open-source data framework (48,000+ GitHub stars, 10M+ monthly PyPI downloads) originally built for retrieval-augmented generation that has evolved into a comprehensive platform for building AI agents over documents. It provides LlamaParse (enterprise document parsing for 90+ file types), Workflows (multi-step orchestration), and LlamaExtract (structured data extraction).

LlamaIndex core strength is connecting LLMs to private data with specialized retrieval techniques: hierarchical chunking, auto-merging, sub-question decomposition, and built-in re-ranking. It integrates with 300+ packages and benchmarks 35% better retrieval accuracy than alternatives.

The tradeoffs: LlamaIndex is document-centric, not collaboration-centric. It has limited multi-agent orchestration compared to CrewAI or LangGraph, fewer integrations for action-oriented tasks (sending emails, updating CRMs), and is not ideal for stateful long-running workflows. We pair it with complementary tools to build complete agent solutions.

How We Use LlamaIndex to Build Your Agents

Enterprise Document Q&A Agents

We build agents that parse, index, and answer questions over your document collections (contracts, manuals, policies, product specs) using LlamaParse for accurate extraction and advanced RAG.

Automated Document Processing Workflows

We deploy agentic document workflows that combine parsing, extraction, validation, and routing, turning messy document intake (invoices, applications, forms) into structured data and automated actions.

Knowledge Base Construction and Maintenance

We use LlamaIndex to build and continuously update searchable knowledge bases from your documents, with automatic re-indexing when documents change and freshness tracking.

Financial and Compliance Analysis

We build agents that analyze financial reports, extract key metrics, cross-reference regulatory requirements, and generate compliance summaries using LlamaIndex complex document understanding.

Data Extraction and Spreadsheet Automation

We use LlamaExtract to turn unstructured documents (catalogs, reports, invoices) into structured data in spreadsheets, databases, or client systems, automating manual data entry.

Hybrid RAG With Multiple Data Sources

We combine LlamaIndex document indexing with external data sources (databases, APIs) to build agents that synthesize information from structured and unstructured data for comprehensive insights.

Example Agents Built With LlamaIndex

Contract Review Agent

Parses contracts using LlamaParse, extracts key clauses (termination, liability, payment terms), compares against standard templates, flags deviations, and generates a risk summary.

Product Catalog Q&A Agent

Indexes product catalogs (PDFs, spreadsheets, spec sheets), answers customer questions about specifications, availability, and pricing, and generates comparison tables on request.

Insurance Claims Processing Agent

Receives claims documents, extracts relevant information, validates against policy terms, calculates coverage, and routes to the appropriate adjuster with a pre-filled assessment.

Employee Handbook Agent

Indexes all HR documents and policies, answers employee questions about benefits, leave, expenses, and compliance with source citations for every answer.

Supplier Document Management Agent

Processes incoming supplier documents (certificates, invoices, compliance docs), extracts and validates key data, flags expired certifications, and maintains a structured supplier database.

Research Synthesis Agent

For professional services firms: indexes industry reports, academic papers, and market data, answers complex research questions by synthesizing across sources with proper citations.

Why Let Us Handle LlamaIndex?

Getting good answers takes real expertise

How your documents are processed and indexed directly affects answer quality. Getting this right requires understanding both LlamaIndex and your content.

Things break and need someone watching

Document formats change, indexes need refreshing, and retrieval quality can drift over time. Someone needs to keep your knowledge pipeline accurate.

Your time is better spent on your business

Every hour tuning search indexes and document parsing is an hour not spent on your customers or growth. Let us handle the technical side.

We manage your knowledge pipeline. Your agent gives answers you can trust.