DocuOCR is the super.AI alternative for teams that want accurate document data extraction without adopting a managed human-in-the-loop crowd or an enterprise sales cycle. It classifies a mixed file, reads any layout, extracts the fields you define, checks them, sends only uncertain values to a built-in reviewer your own team runs, and exports clean data, with self-serve per-page pricing and nothing to deploy first.
Built for teams that looked at super.AI and decided they would rather not route their documents to an external crowd or book a demo to get a price: business users get a dashboard, developers get one REST API, and you start on your own files the same day.
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Drop in a document you would run through super.AI and watch DocuOCR classify it, read it, and return named fields, free, no crowd review and no signup required.
super.AI is a capable enterprise platform. It combines large language models and computer vision with its Data Processing Crowd, a curated, on-demand human workforce that handles labeling, post-processing, and exception handling, and it routes each task to the best AI, human, or software worker so accuracy improves as the system learns. For a large organization with hard accuracy targets and the budget for guaranteed-accuracy SLAs, that managed human-in-the-loop model is a real asset. The reasons teams shop for an alternative usually come down to two things: who handles the hard documents, and how the platform is sold.
Because exceptions can be routed to an external human crowd, the data in your documents, names, account numbers, and other sensitive details, can be handled by people outside your team, which some organizations cannot allow for compliance or privacy reasons. super.AI is also an enterprise engagement: pricing is not published, it is custom-quoted, so you book a demo and size the cost with sales, and the managed human review is bundled into that number, which makes the all-in cost hard to forecast before a sales cycle. For a mid-market team, or anyone with a single workflow to automate, that is a lot of weight to take on for the result they actually want, which is clean data out of their documents.
DocuOCR takes the focused, automated route. Instead of a managed crowd and an enterprise contract, you tell it which fields you want and it uses AI to read those fields on any layout, classifies a mixed batch automatically so the right extraction runs on each file, validates the values against your rules, routes only the low-confidence reads to a built-in review screen your own team operates, and exports clean data through a dashboard for business teams and one REST call for developers, with self-serve per-page pricing. No external crowd touches your documents, and there is no demo to book. You can test it on your own documents this week to see the accuracy on your layouts and the all-in cost before you change anything.
Both apply AI to document data extraction. The difference is how the hard documents get handled and how it is sold: a focused, ready-to-use product your own team runs the same day, versus an enterprise platform that routes exceptions to a managed human crowd. This is an honest look at where each one fits.
| Factor | DocuOCR | super.AI |
|---|---|---|
| Product type | Focused, ready-to-use extraction product | Enterprise IDP platform with a managed crowd |
| Best fit | Teams that want extraction running the same day | Large enterprise wanting guaranteed-accuracy SLAs |
| Getting started | Self-serve, start on your own files today | Book a demo, enterprise onboarding |
| Who reviews hard reads | Your own team, in a built-in screen | super.AI Data Processing Crowd (external workers) |
| Specialized documents | Template-free, reads any layout | LLMs and computer vision, plus human correction |
| Classification | Sorts a mixed batch automatically | Built into the managed workflow |
| Data handling | Documents stay in your workflow | Exceptions can route to an external crowd |
| Moving data out | Dashboard, export, and one REST API | Output routed to your systems |
| Pricing model | Self-serve, per page, workflow included | Custom enterprise quote, managed review bundled |
| Try before you buy | Free on your own files, no signup to test | Start for free, production is enterprise-quoted |
If you are a large enterprise that wants guaranteed-accuracy SLAs and a managed human crowd to handle exceptions, super.AI is built for exactly that. If you want accurate extraction your own team controls, DocuOCR is built on intelligent document processing: it classifies, reads, extracts, validates, and exports, so your people review data instead of handing documents to an outside crowd. For background first, read our explainer on how Super.AI combines AI and crowd work.
Start with who you want handling the hard documents. If you would rather keep that in-house, these are the things that decide whether an alternative fits how your team works and a budget you can plan around.
Look for a built-in review step your own team operates, so sensitive documents never route to an external human crowd to hit an accuracy number.
Favor a product you can start on your own documents now, with no demo to book, no managed-services onboarding, and no enterprise sales cycle first.
Choose AI that reads the fields you define on any layout, so you are not tuning a configuration per document type as your documents vary.
Sorts a stack of different document types automatically, so no one pre-separates files before the right extraction runs.
Self-serve per-page pricing tracks actual usage and is easy to forecast, unlike a custom enterprise quote with managed labor bundled in.
Lets you check accuracy and the all-in cost per page on the exact documents you process, free and without a signup or a sales call.
On security, the data in your documents often includes names, account numbers, and other sensitive details, so DocuOCR keeps review in-house and supports your recordkeeping with encryption in transit and at rest, role-based access, a full audit trail of every extraction and review, configurable retention, and US data handling. How records satisfy an internal control or an audit depends on how a system is configured and operated, so ask us about your specific requirements and deployment.
Classify, read, extract, validate. Drop a file in and the whole sequence runs on its own, with review in-house and no external crowd in the loop.
The engine reads a mixed batch and sorts it by document type, so the right extraction runs on each one without anyone separating the stack first.
OCR and ICR convert PDFs, photos, faxes, and scans into machine-readable text, including handwriting and stamps, without a template tuned per layout.
DocuOCR pulls the values tied to their labels and returns the fields you defined, on any layout, so you get structured data instead of just recognized text.
Values run through your rules, low-confidence reads route to your own reviewer, and clean data exports to a spreadsheet or your systems by API, with an audit trail.
# claim_form.pdf -> extracted data (any layout, no crowd review) { "doc_type": "insurance_claim", "claimant_name": "Marcus Whitfield", "claim_number": "CLM-90413", "loss_amount": "7240.00", "confidence": 0.98 } # classified, read, validated, ready for export
Teams that priced out super.AI and decided the managed crowd, the enterprise quote, and the demo cycle were more than their workflow called for.
Handle PII, financial, or health documents and would rather not route exceptions to an external human crowd to hit an accuracy target.
Want enterprise-grade extraction without an enterprise sales cycle, a managed-services engagement, or a custom quote to size.
Want a dashboard to process documents and review results in-house, without adopting a managed-crowd platform.
Receive mixed stacks of invoices, statements, and forms and want classification to sort them automatically before extraction.
Want self-serve per-page pricing they can forecast, instead of a custom enterprise quote with managed human review bundled in.
Prefer a product they can start on their own files this week over a platform that takes a demo and an onboarding cycle to stand up.
super.AI pairs its API with a managed human crowd for exceptions. DocuOCR works differently: you post a document to a single endpoint and get back the classified type, the recognized text, and the extracted fields with a confidence score on every value, on any layout, with the review, validation, and export steps already in the product and run by your own team. There is no external crowd in the loop and no managed-services engagement to set up before you call it.
# classify + extract in one request curl https://api.docuocr.com/v1/extract \ -H "Authorization: Bearer $KEY" \ -F "file=@scanned_document.pdf" \ -F "classify=true" # -> doc type + named fields + confidence
super.AI does not publish enterprise pricing; it is custom-quoted, so the number depends on a demo, your volume, and how much managed human review you need, which makes it hard to forecast up front. Check super.AI for a current quote. DocuOCR is priced per page with classification, review, validation, and export already in the product, no managed-labor line item and no demo to book before you can start, so you pay for the pages you actually process. Start free to check accuracy on your own documents, then pay per page as your volume grows, with lower committed rates for high volume.
The questions teams ask most when they compare super.AI's managed-crowd platform with a focused, ready-to-use document data extraction product.
The best alternative to super.AI depends on whether you need a managed human-in-the-loop crowd or a product your own team runs. super.AI pairs AI with its Data Processing Crowd, a curated external workforce that handles exceptions, and it is sold as an enterprise engagement. If what you actually need is accurate extraction you control end to end, a focused, self-serve product fits better. DocuOCR classifies a mixed file, reads any layout, extracts the fields you define, validates them, routes only low-confidence reads to a built-in reviewer your own team operates, and exports clean data through a dashboard and one REST API, with transparent per-page pricing you can start today.
super.AI is used by large enterprises to automate document-heavy processes such as customer onboarding and KYC, lending, claims processing, and supply-chain paperwork. Its platform combines large language models and computer vision with a managed human-in-the-loop crowd, so hard or low-confidence documents get routed to people who correct them and the system learns from the feedback. It is built for enterprise volume with guaranteed-accuracy SLAs. Teams that mainly need the extracted data, without adopting a managed-crowd enterprise platform, tend to look at a focused, self-serve alternative.
super.AI extracts data from documents by routing each task to the best AI, human, or software worker. It breaks a document into smaller tasks, sends low-confidence ones to its Data Processing Crowd for human review, and feeds the corrections back so exception rates fall over time. It handles virtually any document type and extends to images, audio, and video. DocuOCR covers the document extraction part, classify, read, extract, validate, review, export, as a focused product with the review step built in and run by your own team, so you get clean structured data without a managed crowd.
super.AI offers a Start for free option to try the platform, but it is not free for production use. Enterprise use, where the guaranteed-accuracy SLAs and the managed human review live, is custom-quoted, so you book a demo and contact sales for a number tied to your volume and requirements. DocuOCR takes a different approach: you can process documents free to check accuracy on your own files first, then pay per page for what you process, with classification, review, validation, and export already included, and no demo required to get a price.
super.AI does not publish enterprise pricing; it is custom-quoted, so you book a demo and contact sales for a figure tied to your document volume, accuracy targets, and how much managed human review you need. Because human-in-the-loop labor is part of the model, the all-in cost reflects that managed work and is hard to forecast before a sales cycle. DocuOCR keeps it self-serve and per page: one price that already includes classification, review, validation, and export, so you can forecast cost from your own volume. Check super.AI for a current quote.
super.AI's Data Processing Crowd is a curated, on-demand human workforce built into its platform that handles data labeling, post-processing, and exception handling. When the AI is not confident, the task is routed to a person who reviews or corrects it, and the correction trains the system so similar exceptions drop over time. It is how super.AI backs its accuracy guarantees. The trade-off is that an external human workforce can handle your documents, so teams with sensitive data, names, account numbers, medical or financial details, often prefer review that stays with their own staff. DocuOCR keeps the review step in-house, in a built-in screen your team operates.
super.AI extracts data with large language models and computer vision, then routes anything the AI is unsure about to its human-in-the-loop crowd for correction, learning from the feedback so accuracy improves with volume. It is designed for enterprise scale with guaranteed-accuracy SLAs. DocuOCR is ready to use and automated: you define the fields you want and it reads them on any layout with AI, classifies a mixed batch so the right extraction runs on each file, validates the values, and routes only low-confidence reads to a reviewer your own team operates, with no external crowd in the loop.
Start with who you want handling the hard documents. If you would rather not route sensitive files to an external human crowd, look for automated AI extraction with a built-in review step your own team runs. Then look for template-free reading that handles any layout, document classification so a mixed batch sorts itself, schema-based output that returns named fields, and both a dashboard for business users and an API for developers. Prefer self-serve per-page pricing over a custom enterprise quote, so cost tracks your usage and you can forecast it, and favor a product you can try free on your own documents and start the same day without a demo. Then confirm the security controls, encryption, access control, audit logging, and where your data is handled, before you move production volume.
A quick primer on Super.AI's IDP platform before you weigh it against DocuOCR.
The end-to-end IDP workflow that classifies, reads, extracts, and validates documents in one pipeline.
The full platform behind the comparison, with a dashboard for teams who want document data without code.
The single REST call that returns classified type, text, and named fields for your own automation.
Comparing DocuOCR with Hyperscience, another enterprise IDP platform, for teams weighing a configured rollout against a ready-to-use product.
Comparing DocuOCR with Instabase AI Hub, an enterprise platform you build automation apps on, against a ready-to-use product.
Comparing DocuOCR with Rossum, a named super.AI peer, for teams weighing a transactional document platform against general-purpose extraction.
Upload a document you would run through super.AI, watch DocuOCR classify it, read it, and return named fields with no external crowd in the loop, then use the dashboard or connect the API to process every document that follows on its own.
