docbatch.ai

docbatch.ai is a document formatting tool. A credit-based batch service that extracts structured fields from invoices, receipts, forms and contracts, returning JSON, CSV or Excel. Cheaper than real-time APIs because jobs run off-peak and finish within hours rather than seconds.

docbatch.ai is a batch document processing service that pulls structured data out of PDFs and images and returns it as JSON, CSV or Excel. The pitch is straightforward: the same underlying models used by real-time AI APIs, applied to work that does not need an instant answer. Jobs run during off-peak hours, which the company says cuts processing cost by up to 70 percent compared with calling those APIs directly. Supported inputs are PDF plus JPEG, PNG, WEBP and GIF, covering invoices, receipts, contracts, resumes, forms and medical records. The workflow has three parts. A sample document is uploaded and the system proposes fields automatically. The user then finalizes the extraction schema, either by describing what is wanted in natural language or by selecting regions visually. After that, files are uploaded in bulk and processed against the saved schema, with results downloadable when the batch finishes. Most jobs complete within one to two hours, with a 24 hour ceiling, and an email notification marks completion. Each job reports an accuracy score, which the vendor puts at 90 to 98 percent for clear, well-formatted documents. This fits back-office and operations teams sitting on recurring document piles: finance groups clearing quarterly invoice backlogs, logistics firms keying in receipts, clinics digitizing forms, recruiters parsing resumes. Anyone who has scripted a direct API call and handled rate limits, retries and parsing will recognize the audience. The tradeoff is latency by design. Results are not immediate, so the product is a poor match for anything interactive, such as a live intake form or a customer-facing upload flow. The stated 99 percent accuracy headline also deserves skepticism; the FAQ's own 90 to 98 percent figure is the number to plan around, and consistent document types batched together perform best. Privacy terms are explicit: files are encrypted in transit and at rest, processed in isolated environments, never used for model training, and deleted after successful processing. Pricing is credit-based rather than subscription, with one credit per document and packs from 1,000 to 20,000 credits. There is no mention of a public API, webhooks or integrations with accounting or storage tools, so teams expecting a pipeline connection should verify that before buying credits.