What teams build on the pipeline.
Four stages. One backend. Open underneath. Each use case below maps to one or more pipeline stages — Acquire, Extract, Enrich, Embed & Retrieve. Pick the stages you need; the rest stay out of your way.
RAG Pipeline Ingestion
Turn a pile of PDFs, Office docs, and HTML into clean, chunked, embedded data for your vector database — in one call.
Document-Reading Agents
Give your AI agents one tool to read any document — 100+ formats, structured output, every framework.
Replace Legacy IDP
Swap brittle, template-based processing for one API that returns schema-mapped JSON — no templates to maintain.
Bulk Archive Digitization
Structure millions of legacy and scanned documents for search and ML — built for batch on a Rust-native core.
Syntax-aware Code Chunking
Turn source code in 306 languages into structure-aware chunks — ready to embed for code search, assistants, and RAG.
Web Content Ingestion
Crawl the hard web — JavaScript-heavy sites and pages behind bot protection — into clean Markdown and structured content.
Semantic Search over Documents
Build semantic search over your documents with built-in chunking and ultra-fast embeddings — no separate pipeline.
Regulated & Self-Hosted Extraction
Run the same extraction engine self-hosted or air-gapped, for regulated data that can't leave your environment.
Pre-Built Document Extraction
Name the document type — invoice, receipt, W-2, contract, ID — and get clean, typed fields back. No templates.
Form-Field Extraction
Read the answers straight out of fillable PDFs — exact field names and values, not OCR guesses.
Handwriting & Messy Scans
Read handwriting, faded carbon copies, and photographed receipts with a vision model — where classic OCR returns garbage.
Documents in Any Language
Detect the language automatically and OCR across a wide range of scripts — Arabic, Chinese, Cyrillic, Hindi, Japanese, and more.
Redact Sensitive Data
Detect personal information across many categories and mask, hash, or tokenize it — with a report of what was found and where.
Summarize Long Documents
Turn 80-page contracts, reports, and filings into a tight, grounded summary — extractive or abstractive.
Translate Documents
Translate extracted content into another language while keeping structure — headings stay headings, tables stay tables.
Classify & Route Documents
Answer “what is this?” automatically — invoice, contract, complaint, resume — and route each document to the right queue.
Extract Entities (NER)
Pull the who, when, and how-much out of free-form text — people, organizations, locations, dates, and amounts.
Pull Keywords & Topics
Auto-tag documents with their keywords and key phrases for findability — no manual curation.
Extract Equations & Formulas
Lift the math out of scientific and engineering documents as clean LaTeX, with its position on the page.
Split Combined Scans
Detect document boundaries inside one big scanned PDF and split it into separate, labelled documents.
Extract Track-Changes & Revisions
Surface the tracked changes and revision history embedded in Office documents and PDFs — who changed what.
Compare Document Versions
Get a reliable diff between v1 and v2 — additions and removals highlighted, for prose and tables.
Fire-and-Forget Processing
Submit a job and get a webhook when it is done — no holding a connection open per document.
Transcribe Audio & Video
Turn meetings, calls, and webinars into searchable, structured transcripts — in the same pipeline as your documents.
Describe Images & Charts
Generate descriptions of figures, charts, and diagrams inside a document — so your RAG system can see them.
Read QR Codes & Barcodes
Detect and decode QR codes and barcodes found in document images — shipping labels, tickets, inventory sheets.
Parse Citations & Footnotes
Pull references and footnotes out as structured data, and tie excerpts back to the claims they support.