In developmentD2Transform is designed to transform documents into clean, structured, high-quality data that is easier to review, manage and use in digital workflows.
The problem
Businesses work with information spread across vouchers, statements, invoices, certificates and other documents. Extracting, organizing and validating it is slow when it depends on manual effort.
D2Transform focuses on making document-based information easier to process and use.
Bank deposit vouchers and account statements, read field by field.
Totals, dates and parties as named fields instead of retyped values.
Citizenship and other certificates, including Nepali text and Bikram Sambat dates.
Excel, CSV, Word and text files go through the same review and mapping.
How it works
Each document moves through the same five stages, and the raw result is always kept unchanged.
Digital and scanned PDFs, photos and scans (JPG, PNG, TIFF, HEIC, WebP), Excel, CSV, Word and text files.
Digital PDFs are read from their text layer; scans and photos go through OCR.
Values become named fields, kept exactly as written; Devanagari digits and Bikram Sambat dates also get a converted value alongside the original.
Each value is validated and scored. Anything below the confidence threshold set for the document type goes to a person to review.
Reviewed data is mapped to the organization's own schema and exported as a table, JSON or CSV.
Review and validation
Doubtful values are routed to human review rather than passed through silently. Documents that pass every check can be approved automatically; the rest wait for a reviewer.
Data ready for your workflows
Map extracted fields to your own schema and export them, or deliver them to your systems with signed webhooks.
Capabilities
Turn the contents of vouchers, statements, invoices and certificates into named fields instead of retyping them.
Map extracted fields to your own schema and export them, or deliver them to your systems with signed webhooks.
Every value carries a confidence score, and doubtful values are routed to human review rather than passed through silently.
Documents that pass every check can be approved automatically; the rest wait for a reviewer.
Nepali documents are read as written — never translated — with Devanagari digits and Bikram Sambat dates converted alongside the original, and Preeti-encoded pages read by OCR.
Organizations, roles and permissions are built in, so each team works only with its own documents.
Case study
How we are building a document-to-data platform that reads PDFs, photos and Office files, keeps every value exactly as written in English or Nepali, scores it, and sends anything doubtful to a person before it reaches an export or a webhook.
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D2Transform is in development. Tell us about the documents you work with and we can discuss whether it fits your needs.