Automate document processing with AI-powered API

Mindee is designed with a "developer-first" philosophy, offering an intuitive interface that replaces complex manual programming with visual training tools. No training is required before using a model, while expecting high-accuracy for any document types.

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This democratization of AI allows product teams to deploy custom classification skills in a fraction of the time it would take to build a traditional OCR pipeline from scratch on GitHub.

When it comes to automated document data extraction, Here are the most impactful real-world examples of how automated data extraction is used to eliminate manual entry:

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In the accounts payable world, data extraction is the engine behind "touchless invoicing."When a vendor sends an invoice, the API doesn't just read the text; it extracts specific fields like the invoice number, tax ID, net amount, and total. Most importantly, it parses complex line-item tables, capturing every individual product, quantity, and unit price.

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This allows the system to process payments automatically while ensuring that credit notes and statements are recorded with 100% accuracy.

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It is a core component of two-way matching and reconciliation for supply chain management. By extracting the PO number from a purchase order and the SKU list from a corresponding delivery note, businesses can automatically verify that the items received match the items ordered. This automated extraction ensures that auditors have a clean, digital trail of exactly what was delivered versus what was requested, without a human ever having to cross-reference paper sheets.

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For customer onboarding, automated extraction turns a slow, manual verification process into an instant check. When a user uploads their ID or a utility bill, the API extracts the full name, date of birth, and document expiration date. It also pulls the address and account number from utility bills to provide instant proof of residence. This allows companies to verify a customer’s identity in seconds, significantly reducing drop-off rates during the sign-up flow.

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You can check more real-life examples of how companies leverage this technology by visiting customer stories.

Yes,with Mindee,you can test this feature by signing up for free here and uploading a sample file. Line items and complex tables recognition will be fully supported from PDFs or any image formats.

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This is where generalist APIs often fail. Standard OCR might give you a "word soup."

For complex tables (multi-line rows, merged cells, or nested headers), you need a vision-aware pipeline.

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Pro tip: Generalist LLMs often hallucinate table structures. For "messy" financial documents, look for APIs that use specific vision models rather than just generic text-to-text models

With Mindee, you can handle up to 100MB size per file and up to 200 pages.

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Large files (e.g., 100-page mortgage) should never be processed in a "request-response" (synchronous) loop. We can talk about two API methods :

• Asynchronous processing (Polling) : You submit the file, receive a job_id, and the API processes it in the background.

• Webhooks : Once complete, the API "pings" your server with the structured JSON. This is the gold standard for any Automated data extraction API setup for every language (Python, Node JS, Java, etc.)

Getting JSON is step one; getting valid JSON is step two. Most modern APIs, like Mindee allows you to define a data schema. To ensure your database doesn't crash:

• Use Pydantic (Python) or Zod (TypeScript) to validate the API output.
• If the extraction doesn't meet the schema (e.g., a missing mandatory invoice_id), flag it for human review.

No. Unlike traditional "On-Premise" solutions that take months to deploy, Mindee’s API-first approach allows you to go live in minutes. The complexity of document processing usually depends on whether you choose to build an infrastructure or consume an intelligent service.

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Mindee simplifies the experience by two ways :

• Zero-training time : Mindee models are pre-trained on millions of data points. Whether it’s an invoice, a receipt, or an ID card, the API already "knows" what it’s looking at. You don't need a Data Science team; you just need an API key.
• Plug-and-play : Mindee is a plug-and-play REST API. With our dedicated SDKs (Python, Java, PHP, and more), a developer can integrate full document automation into your existing software with just a few lines of code.

Mindee’s API is RESTful and returns data in JSON format. XML is not returned.

To connect Mindee to your business tool you can use Mindee’s REST + JSON API via an ERP/CRM connector, HTTP steps, or webhooks. Most ERP/CRM tools integrate either through HTTP actions/nodes (low-code) or an automation platform.

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If your ERP/CRM supports inbound webhooks, you can also use Mindee webhooks to receive results on your server endpoint (recommended for heavy production usage).

Mindee’s pricing is structured to support businesses at every stage, from early testing to high-scale production. We offer four main subscription plans—Starter, Pro, Business, and Enterprise—each with a fixed monthly page volume and access to different features.

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• Starter, Pro, and Business plans include a monthly page allowance (ranging from 500 to 10,000 pages), with additional pages charged at a per-page rate starting from $0.05.
• Enterprise plans are fully customizable and designed for organizations with specific volume, SLA, or integration needs.

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Advanced features such as RAG capabilities, automation tools, and premium support are included progressively across the plans.

This model allows you to choose the plan that best fits your current needs while retaining the flexibility to scale as your usage grows.

You can calculate your specific breakeven point, but in short :

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BUY if :

• You are processing standard documents (Invoices, IDs, Receipts, Tax forms)
• You need speed-to-market and high accuracy immediately
• Document processing is a utility/commodity for your business, not the product
• You want predictable monthly spending without managing GPU hardware

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BUILD if :

• Your documents are highly proprietary (e.g., secret blueprints, unique internal forms)
• You have strict data sovereignty requirements (Data cannot leave your private cloud)
• You want to own the Intellectual Property for a core business differentiator
• You process >30M pages/year, making the "API tax" higher than a 5-person engineering team