Extend
Extend · Productivity
Extend is an AI document processing platform built for developers who need to turn messy files into clean data. Its APIs handle parsing, extraction, splitting, classification, and editing, with specialized vision models reading tables, handwriting, checkboxes, and signatures that basic OCR tools miss. Teams at Brex, Square, Amgen, and CH Robinson use it to build document agents that run in production, and pricing starts free with 10,000 credits before you pay per page.
About Extend
What Is Extend
Extend is document processing infrastructure, not a consumer app. You send it a file through an API, and it returns structured output your software can use: text with layout preserved, fields pulled into a schema you define, or a stack of documents cut into individual pieces. The pitch is accuracy on hard documents. Not clean, well-behaved PDFs. The hard ones. Think a phone snapshot of a crumpled invoice, a 400-page financial filing full of dense tables, a scanned contract with handwritten notes in the margin. Extend routes each element to a purpose-built model instead of pushing everything through one general model.
The people who get the most out of it write code. But not always much of it. Extend ships SDKs for Python, TypeScript, Java, and Go, plus a CLI, so a document pipeline is a few lines of setup rather than a custom OCR project. A studio and evaluation tools sit alongside the APIs for teams that want to test accuracy before committing, which is the part most people skip and regret later.
The main limit is scope. It's plumbing for AI agents and data pipelines, nothing more. Extend isn't a place to chat with your documents, and it isn't a no-code tool for casual users. There's no mobile app. There's no drag-and-drop interface for someone who just wants to summarize a PDF. You need a developer to wire it in.
Getting Started
- Create an account at the Extend dashboard and claim the 10,000 free credits included on the pay-as-you-go plan.
- Install an SDK, for example
pip install extend-aifor Python, and grab an API key from the dashboard. - Pick the endpoint that matches your job: parse for raw content, extract for schema-based fields, split for multi-document files, or classify to sort by type.
- Define your output schema or select a preset one, then run a small batch to check accuracy before scaling up.
- Review results in the studio, tune your configuration, and promote the pipeline to production.
Product Information
A quick look at Extend's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Backend and AI engineers
- Operations teams at document-heavy companies
- AI product teams building agents
Tasks
- Extracting fields from invoices and receipts
- Splitting combined PDFs
- Parsing dense financial tables
- Classifying incoming documents
- Structured data extraction
Scenarios
- Migrating off a brittle in-house OCR system
- High-volume batch processing
- Real-time document intake
Key features
Layout-Aware Parsing
The parse API reads a document the way a person would, detecting tables, checkboxes, images, handwriting, and signatures on every page. A hybrid pipeline combines computer vision with vision-language models, routing each element to the model best suited for it. The result is text plus structure, not a flat wall of characters. That's the whole point.
Schema-Based Extraction
You describe the JSON you want, and the extract API fills it in from the document. It scales to files over 1,000 pages using chunking and merging strategies, so long documents don't blow past context limits. Large tables with 1,000+ rows come out intact.
Citations and Bounding Boxes
Every extracted value gets a citation back to the exact spot in the source document, including values the model inferred. For anyone who needs to verify output before trusting it, this is the feature that makes the platform usable in regulated work.
Document Splitting and Classification
The split endpoint breaks multi-document files into individual subdocuments, holding accuracy on files over 2,000 pages and handling mid-page boundaries. Classification sorts documents into categories you define, so a mixed batch gets organized before extraction even starts.
Performance Modes
Extend lets you trade off speed, cost, and accuracy per job. Fast mode targets real-time use, while cost-optimized modes suit bulk work where you don't need the highest accuracy on every page. Light Parse, added in August 2026, pushed cost down further at $0.00625 per page for straightforward documents.
Multi-Language and File Support
One API handles 35+ file types and over 100 languages, with four chunking strategies to choose from. That range matters if your documents arrive in a mix of formats and languages, which is common in global operations.
Model-Agnostic Design
Extend continuously benchmarks new foundation models and lets you upgrade versions safely after evaluating them on your own documents. You're not locked to one model, and you can test whether a swap actually improves your results before shipping it. That flexibility is rare.
Pros and cons
Pros
- One platform covers parsing, extraction, splitting, classification, and editing, so you don't stitch together separate vendors.
- Citations and bounding boxes on every extracted value make outputs verifiable, which matters for legal, finance, and compliance work.
- Free tier with 10,000 credits lets you test accuracy on real documents before spending anything.
- SDKs for Python, TypeScript, Java, and Go mean most backend teams can integrate without extra tooling.
Cons
- There's no no-code interface or consumer app, so a non-technical user can't just upload a PDF and get answers. You need a developer.
- Pricing is credit-based and page-dependent, which makes costs harder to predict than a flat monthly fee until you know your document mix.
- The highest accuracy on messy documents costs more per page, so budget-conscious teams may need to sort documents by difficulty first.
Frequently asked questions
Extend turns unstructured documents into structured data through APIs. It parses files to preserve layout, extracts fields into a schema you define, splits multi-document files, and classifies documents by type. It's meant to feed AI agents and data pipelines rather than serve as an end-user app.
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