Strategic Goal

To automate end-to-end purchase order processing, from document intake and data extraction through validation and ERP posting, reducing manual effort, improving data accuracy, and scaling consistently across multiple countries.

Challenges

The company’s Purchase Order (PO) processing was held back by the sheer diversity

and inconsistency of how customers submitted their orders:

  • Multiple formats and non-standard layouts: Purchase orders arrived as PDFs, Excel files, scanned images, Word documents, and plain text files, each with its own structure, field placement, and product code conventions.
  • Time-consuming manual validation: Teams had to manually identify the correct customer record, verify each product code against ERP master data, and re-enter all relevant data line by line, consuming hours per day across multiple regional teams.
  • Inconsistent customer and product identifiers: Customers across Europe and the Americas used varying codes and naming conventions, making automated matching against ERP records unreliable without a structured validation layer.
  • No structured exception handling: When data was missing or inconsistent, there wasno systematic workflow to route documents for review, leaving teams to manage discrepancies manually and inconsistently.
  • Risk of ERP input errors: With high volumes of manual entry, the risk of mistakes reaching the ERP was significant, with downstream consequences for order fulfilment and customer satisfaction.

Solution

CLARITY deployed an end-to-end automated PO workflow, enabling fast, accurate, and

fully auditable PO processing from intake through to ERP posting.

  • Multi-format document ingestion: The solution automatically ingests purchase orders in all incoming formats, including PDF, DOCX, XLS/XLSX, TXT, and PNG/JPG scans, classifying each by file type and routing it to the appropriate processing flow.
  • Automated field extraction: Key data is extracted automatically from every document, covering header fields such as customer details, PO number, date, currency, and commercial terms, as well as line items including product codes, descriptions, quantities, unit of measure, pricing, and requested delivery dates.
  • Customer identity resolution: To ensure the correct ERP customer number is used, the solution cross-checks customer names and address details, including street, city, ZIP code, and country, against existing ERP master data. Where a match is unclear or duplicates are detected, the document is automatically flagged for human review.
  • Item validation against ERP master data: Every product code and line-item detail is validated against ERP master data in real time. Missing, conflicting, or unrecognized data triggers automatic routing to an exception queue rather than passing through to the ERP.
  • Straight-through processing for clean documents: Documents that pass all validation checks are automatically approved and posted directly into the ERP via API, along with the original source email and attachments for full traceability.
  • Human review for exceptions only: Documents that fail validation are presented in a structured review queue with clear flags indicating the reason, such as customer ID notfound, product code not recognized, or missing quantity or price. Reviewers can correct data directly in the interface, send a contextual email to the supplier to request missing information, or update master data records without leaving the platform.

Results

The CLARITY solution delivered significant operational impact across the customer’s global processing teams:

  • High-volume automation at scale: The solution processes approximately 2,000 purchase order documents and 4,500 pages per month across six global teams, handling the full diversity of formats and layouts.
  • Majority of documents processed without human touch: With a 60% straight-through processing rate, around 1,200 documents per month are completed automatically with no manual intervention, significantly reducing ERP input effort and eliminating repetitive data entry work.
  • Human effort focused on exceptions: The remaining 40% of documents are routed for review and approval, allowing teams to direct their attention to cases that genuinely require judgement rather than routine processing.
  • Improved data accuracy: Validation logic catches missing, conflicting, or unrecognized data before it reaches the ERP, reducing input errors and improving data consistency across all markets.
  • Scalable operations without proportional headcount growth: The same automated workflow was rolled out across six countries in a matter of months, enabling the company to handle growing order volumes without expanding its processing teams.