Invoice & Accounts Payable Processing
Automated invoice capture with human verification on flagged exceptions, reducing manual entry without sacrificing accuracy.
Advanced & 3D Labeling
High-accuracy OCR paired with human-in-the-loop review to digitize forms, invoices, and archives at scale.
Automated OCR handles the bulk of the work — but the remaining fraction, where fields are misread, handwriting is ambiguous, or a scan is degraded, is exactly where costly downstream errors originate. Vidyut Data pairs automated OCR output with trained reviewers who verify low-confidence fields, correct extraction errors, and map results into your required schema. The result is document data clean enough to feed directly into your systems — even when the source includes handwriting, poor scan quality, or inconsistent layouts.
Automated invoice capture with human verification on flagged exceptions, reducing manual entry without sacrificing accuracy.
Structured extraction from claim forms, medical bills, and supporting documents to speed up adjudication workflows.
Contract and case file digitization with clause-level tagging to support search, discovery, and compliance review.
Converting paper-based archives, ledgers, and legacy records into searchable, structured digital data — including aged or low-contrast scans.
This is exactly where automated OCR struggles, so those fields are routed to human review and correction. Realistic accuracy depends on your document quality — we establish achievable targets together during the pilot rather than promising a blanket number.
Yes. We map output to your key-value and table structure so it posts directly into your downstream systems, rather than handing back raw text you'd have to restructure.
Yes, including multi-script documents. We confirm coverage for your specific languages during scoping.
Documents are handled by an assigned team under access controls, with data-handling and retention terms set per engagement — important for documents containing personal or financial information.
Start with a pilot batch — see the quality of the data before you commit.