Turn Document-Heavy Work Into Structured, Reviewable Data
Invoices, claims forms, applications, certificates and correspondence arrive as PDFs, scans, photos and email attachments. Kramora builds document processing pipelines that classify what arrives, extract the fields that matter, validate them against business rules and send uncertain cases to a reviewer. Every step is logged, so operations teams move faster and can still explain exactly how each record was created.
The Operating Challenge
What the Solution Includes
Ingestion and classification
Documents arrive through email, uploads, scanners, file transfer and APIs, often bundled together in one file. The pipeline splits packets, identifies each document type and routes it to the right extraction logic. Unrecognized documents go to a triage queue instead of failing silently.
OCR when it is required
Digital PDFs already contain text, so OCR is applied only to scans, photos and image-based files. Image cleanup, layout analysis and handwriting recognition are added where the document mix calls for them. Skipping unnecessary OCR keeps the pipeline simpler and more accurate.
Extraction into a defined schema
Each document type maps to a target schema of fields, tables and line items agreed with the business. Extraction combines layout-aware models, language models and deterministic rules depending on the field. Every value carries a confidence score and a pointer to where it was found on the page.
Validation against business rules
Extracted data is checked for format, completeness and internal consistency, then cross-checked against reference data such as vendor lists, policy records or purchase orders. Totals that do not add up or numbers that do not match a known record are flagged with a clear reason. Validation is where most of the business value is protected.
Human review and audit trail
Reviewers see the original document beside the extracted fields, with uncertain values highlighted and validation messages explained. Corrections are captured, logged with the reviewer's identity and fed back into quality reporting. The complete history from intake to posting remains available for audit and dispute resolution.
Illustrative Architecture
Illustrative reference architecture, not a client implementation, showing how a document moves from intake to validated, reviewable business data.
Outcomes to Expect
Claims intake for a specialty insurer
A specialty insurer receives loss notices, repair estimates and photos by email and through a broker portal. Adjusters spend the start of each claim copying details into the claims system and chasing missing documents.
Kramora would map the document types and fields that drive the first adjuster decision, then build classification and extraction with validation against policy data. Complete, high-confidence packets would post directly to the claims system, while incomplete ones would open a review task listing exactly what is missing.
Frequently Asked Questions
Often, within limits. Handwriting recognition and image cleanup can improve results, but accuracy depends heavily on source quality. Discovery includes testing on a representative sample of your real documents, so expectations are set on evidence before the build starts.
Services Involved in This Solution
Relevant Industry Context
Start with the operating challenge.
Tell us where work slows down today. We will help you decide whether this solution fits and what a first phase could look like.