LlamaParse is an agentic, VLM‑powered document processing platform that converts PDFs, scans, images, tables, charts, and handwritten notes into clean, structured outputs for AI workflows. It combines layout‑aware OCR, recursive auto‑correction loops, and semantic understanding to deliver high‑accuracy extraction in formats like JSON, XLSX, HTML, and annotated PDFs via a credit‑based API.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and developers must manually process large volumes of heterogeneous documents—PDFs, scans, images, tables, charts, and handwritten notes—using fragmented tools that are slow, error‑prone, and require extensive custom code.
Solution
LlamaParse provides an agentic, VLM‑powered document processing platform that converts complex, multi‑modal files into clean, structured outputs ready for AI workflows. The service combines layout‑aware OCR, recursive auto‑correction loops, and semantic understanding to extract text, tables, charts, and handwritten content with high accuracy. Parsed data can be emitted in formats such as JSON, XLSX, HTML, or annotated PDFs and is delivered via a credit‑based API that integrates with LlamaAgents for end‑to‑end ingestion, extraction, indexing, and retrieval. Users can configure parsing tiers, enable smart tier routing to reduce costs, and obtain page‑level citations, confidence scores, and bounding boxes for human‑in‑the‑loop review. The platform scales from a free tier with 10 K credits to enterprise plans with private VPC deployment, SOC 2, GDPR, and HIPAA compliance.
Target Audience
Primary customers are software engineers, data science teams, and R&D groups building AI applications that require reliable document ingestion, as well as enterprise operations (finance, insurance, healthcare, manufacturing) that need automated document workflows.
Features
- Agentic OCR that understands complex layouts, multi‑page tables, charts, and messy handwriting
- Auto‑correction loops that recursively detect and fix extraction errors, improving pass‑through rates on noisy scans
- Structured JSON, XLSX, Markdown, HTML, and annotated PDF outputs with schema mapping and custom field definitions
- Cost‑optimizer mode that automatically selects the most efficient parsing tier per page, saving up to 80 % of credits
- Support for 130+ file types and 80+ languages, including embedded images and in‑line graphics
- Confidence scoring, page‑level citations, and precise bounding‑box metadata for auditability
- Scalable credit‑based pricing with pay‑as‑you‑go options and enterprise private‑cloud deployment