Accelerator category
AI-powered tools for splitting, naming, extracting, and routing the documents that flow through AP and accounting workflows. Upload a bundled PDF of invoices and get back individually split, properly named files ready to post.
Reviewing an invoice means bouncing between the PDF on the left and the form on the right and losing your place every time you click a…
Clients upload bundled PDFs of invoices; each one has to be handled separately. Auto Split turns one large PDF into clean, individually named documents, automatically.
Document Processing covers AI-powered tools that handle the document intake, splitting, naming, and extraction work that slows down AP and accounting teams. Currently live: Auto Split, which takes a bundled multi-invoice PDF and returns individually split, properly named files ready to post. Additional accelerators for automated extraction, classification, and routing are in development.
The accelerators handle PDFs as the primary format, including both digitally generated and scanned documents. Standard invoice bundles of several hundred pages process reliably. For scanned documents, OCR runs automatically before extraction. If you have specific file types such as multi-page TIFFs, XPS, or fax output, share samples during the demo and we will confirm compatibility.
Output files are named and structured for direct upload into any accounting or AP platform that accepts PDFs, including QuickBooks, Xero, NetSuite, Sage, and most AP automation systems. Where a platform supports API posting, the accelerator can push extracted data fields (vendor, amount, date, PO reference) as structured records rather than documents.
The tooling uses a multi-pass OCR pipeline with confidence scoring on each extracted field. Fields below the confidence threshold are flagged for human review rather than silently passed through. Accuracy on standard typed business invoices runs above 95 percent. Handwritten annotations and low-resolution fax output are surfaced in a review queue rather than auto-posted.
Documents are processed in a secure environment and extracted data is written back to the client's own system. Satva does not retain document content after processing completes. Specific data residency, retention policies, and audit trail requirements can be confirmed during the scoping call.
Most teams are processing documents in production within two to four weeks of the first scoping call. The bulk of that time is mapping your existing naming conventions and folder structures, not building infrastructure. The core tooling is already running; onboarding is configuration, not development.
Yes. The typical path is a 15-minute demo with a sample file set, followed by a bounded pilot run on your own documents before any contract is signed. This lets both teams verify accuracy against your specific document mix, confirm naming rules, and surface any edge cases before the engagement expands.
Split rules (how to detect page boundaries between separate documents) and naming rules (vendor, date, PO number, custom fields) are configured during onboarding to match your existing conventions. Most teams use a combination of header-pattern matching and positional rules. Rules can be updated after go-live without redeployment: submit a change request and updated rules are applied within one business day.