Documents arrive before the process can move
Teams still receive orders, invoices, reports and field records in formats that require interpretation before any system can use them.
Vertical document workflows - Research and pilot development
From messy business documents to validated, workflow-ready records.
ScanOps is being developed for teams that still read PDFs, emails, scans and field reports by hand before re-entering information into ERP, accounting, compliance, project or operations systems.
The goal is not simply to extract text. It is to complete the document-to-system handoff with evidence, validation and human review where uncertainty matters.
No sales call required. Follow-up participation is optional.
Document-to-system handoff
1Understand
2Match
3Validate
4Review
5Prepare
The goal is not simply to extract text. It is to complete the document-to-system handoff with evidence, validation and human review where uncertainty matters.
ScanOps starts where recurring business processes get stuck.
A document arrives. A person reads it, interprets it, checks it against business context and decides what happens next. ScanOps is being developed for the manual handoff between that unstructured input and the workflow-ready record the business needs.
Business context lives outside the file
A useful record needs the right customer, vendor, location, project, vehicle, asset or reference, not just extracted text.
Trust depends on reviewable evidence
Operational teams need to see what was captured, where it came from and what still needs a person to decide.
Platform capabilities
Understand, match, validate, review and prepare.
ScanOps is being designed around the full document workflow, not a one-step file extraction screen.
Understand varied documents
Extract structured fields, line items, references and operational context from PDFs, email attachments, scans, spreadsheets and other recurring business documents.
Match business context
Connect extracted information to customers, vendors, locations, projects, vehicles, assets or other records required by the workflow.
Validate before action
Apply business rules, required-field checks and system context before data is accepted downstream.
Route exceptions to people
Surface missing, conflicting or low-confidence information instead of silently guessing.
Preserve evidence
Keep important extracted values connected to their source document for review and auditability.
Prepare downstream records
Create structured records suitable for review, export or integration with ERP, accounting, compliance, project and operational systems.
Workflow examples
Buyer-specific paths for document-heavy operations.
These are focused workflows being researched or prepared for pilot development. Facilities service reports remain a dedicated vertical rather than the whole product.
Customer PO Intake
Turn emailed customer purchase orders into validated, ERP-ready sales-order drafts.
Multi-Location Vendor Invoices
Capture invoice data, identify the correct entity and location, check required references and prepare exceptions for review.
Transportation Compliance
Normalize fuel, mileage, trip and supporting reports into review-ready compliance records with source evidence.
Construction Field Reporting
Turn daily reports, notes, timesheets and supporting field documents into structured project-day records.
Facilities Service Intelligence
Preserve contractor service reports as searchable, asset-linked service history with findings and follow-up context.
About the research
Help identify which document workflows are worth piloting.
Japnify is researching recurring document workflows where manual interpretation, validation and re-entry still sit between the document and the next business action.
The survey takes approximately three minutes. It does not require a sales call and contact details remain optional for research participation.
Trust principles
Operational AI must earn trust.
Evidence before automation
Every important result should remain connected to its source.
Human judgment where uncertainty matters
AI should surface uncertainty, not conceal it.
Source evidence retained
Reviewers should be able to trace values back to the document that produced them.
Operational outcomes over AI novelty
The work is judged by whether the next business step becomes easier to trust.