DocBatch.ai extracts specific information from large collections of PDFs and images. Its current platform, Batch AI 2.0, follows a simple workflow: upload a sample, choose the fields you need, submit the documents, and download the results as JSON, CSV, or Excel.

This is more than basic optical character recognition. OCR can tell you which words appear on a page. DocBatch.ai tries to work out whether those words represent an invoice number, due date, customer name, or something else.

That distinction matters when the documents contain the same information but put it in different places.

DocBatch.ai turns batches of PDFs and images into structured fields that can be exported for use in spreadsheets, databases, and other business workflows.

Every job begins with a sample PDF or image.

DocBatch.ai scans the file and suggests fields it may be able to extract. You then create a schema that defines the final dataset, either by describing the fields in writing or selecting them on the document. Once the schema looks right, you apply it to the full batch.

The result should be organized data rather than a long OCR transcript that someone still has to clean manually.

An accounts-payable team, for instance, may need the vendor, invoice number, due date, tax, total, and line items. A recruiter may care about the applicant’s name, contact details, skills, education, and employment history.

The schema tells DocBatch.ai exactly where those details belong in the output.

DocBatch.ai is meant for volume. A batch can contain roughly 10 to 10,000 documents, all processed with the same extraction schema.

The jobs run asynchronously. After submitting a batch, you can leave it alone and wait for a notification instead of keeping the application open.

That suits work such as processing a month’s invoices or converting an archive of forms. It does not suit a checkout or customer-service flow that needs information from one document immediately. Those situations call for a real-time extraction API.

DocBatch.ai accepts PDF, JPEG, PNG, WEBP, and GIF files. The company highlights invoices, receipts, forms, contracts, resumes, and medical records, while custom schemas can cover less predictable document types.

The contract parser supports NDAs, leases, employment contracts, service agreements, and purchase orders. Resume extraction can handle single-column and multi-column layouts, including files with photographs.

Support for a format does not mean every document will be equally easy to parse. A clean digital invoice and a crooked photograph of a faded receipt are very different inputs.

Results are available as JSON, CSV, or Excel files. Developers can use the structured output in technical pipelines, while operations teams can open the same data directly in a spreadsheet. Batch history and retries help with recurring extraction jobs.

DocBatch.ai reports typical accuracy of 90–98% on clear, well-formatted documents. Confidence information is included so users can identify fields that deserve another look.

The important phrase is “clear, well-formatted.” Blurry scans, handwriting, unusual layouts, mixed document types, and ambiguous fields can all lower the quality of the results.

Human review is still necessary when one wrong number could affect a payment, contract, or hiring decision. DocBatch.ai also recommends keeping similar document types together instead of throwing every possible layout into one batch.

DocBatch.ai says uploaded documents are encrypted, processed in isolated environments, excluded from model training, and deleted automatically after successful processing.

Those are useful safeguards, but companies still need to carry out their own security and compliance review. Medical records, contracts, financial documents, and employment files can contain highly sensitive information. They should not be uploaded to any third-party service solely on the strength of a short privacy statement.

DocBatch.ai fits accounts-payable processing, bulk invoice extraction, contract-review preparation, resume parsing, back-office digitization, and the conversion of document archives into structured datasets.

It is most attractive when the alternative is either hours of manual data entry or building an in-house OCR and extraction pipeline.

For five pages, manual entry may still be quicker. For five thousand pages containing the same set of fields, the calculation changes.

Speed is the main tradeoff. DocBatch.ai processes documents as batches, so it is not designed for applications that need an immediate response.

Its developer tools are still developing too. The website currently lists full documentation and an API reference as coming soon, which may rule it out for engineering teams that need a mature API today.

A direct Google Sheets integration is also listed on the roadmap. Until it arrives, teams can use downloadable exports or webhook-based workflows to move the data elsewhere.