DeSL - Google Document AI Integration and Automation

Common Integration Use Cases Between DeSL and Google Document AI

DeSL and Google Document AI complement each other well in fashion and retail product development workflows. DeSL manages structured PLM and supply chain processes, while Google Document AI extracts data from unstructured documents such as invoices, tech packs, certificates, purchase orders, and compliance forms. Integrating them helps teams reduce manual data entry, improve document accuracy, and accelerate product and sourcing operations.

1. Automated extraction of supplier documents into DeSL

Data flow: Google Document AI to DeSL

Supplier-submitted PDFs, scanned forms, and spreadsheets can be processed by Google Document AI to extract key fields such as supplier name, style number, material details, quantities, delivery dates, and pricing. The extracted data is then pushed into DeSL to create or update product records, sourcing entries, or purchase-related workflows.

• Reduces manual rekeying from supplier documents
• Improves accuracy of product and sourcing data
• Speeds up onboarding of supplier submissions

2. Tech pack and specification document indexing

Data flow: Google Document AI to DeSL

Design teams often receive tech packs, spec sheets, and annotated PDFs in inconsistent formats. Google Document AI can classify and extract structured information from these documents, then attach the data and source files to the correct style or product record in DeSL. This supports faster review and better traceability across product development stages.

• Centralizes unstructured design documentation in DeSL
• Helps teams find the latest approved specifications quickly
• Supports better version control and auditability

3. Compliance document capture for material and product approvals

Data flow: Google Document AI to DeSL

Compliance teams can use Google Document AI to extract data from certificates, test reports, declarations of conformity, and regulatory documents. The extracted information can be linked to relevant materials, trims, or finished goods in DeSL to support approval workflows and compliance checks before production release.

• Improves visibility into compliance status by item or style
• Reduces delays caused by manual document review
• Supports faster approval of materials and products

4. Purchase order and invoice reconciliation against PLM records

Data flow: Google Document AI to DeSL

Accounts payable and sourcing teams can process supplier invoices and purchase orders through Google Document AI, extracting line items, quantities, unit costs, and reference numbers. That data can be matched against DeSL product, style, and sourcing records to identify discrepancies before payment or production release.

• Improves financial and procurement controls
• Flags mismatches between ordered and invoiced quantities or prices
• Reduces manual reconciliation effort

5. Supplier onboarding and master data creation

Data flow: Google Document AI to DeSL

When onboarding new suppliers, DeSL can receive extracted data from tax forms, bank letters, insurance certificates, and business registration documents processed by Google Document AI. This enables faster creation of supplier profiles and associated compliance records in DeSL, with less manual validation.

• Shortens supplier onboarding cycle time
• Improves completeness of supplier master data
• Supports standardized intake of onboarding documents

6. Automated attachment and metadata enrichment for product records

Data flow: Bi-directional

Google Document AI can extract metadata from incoming documents, while DeSL can send product identifiers, style codes, and workflow context to help classify those documents correctly. The result is a cleaner document repository where files are automatically tagged and attached to the right product, season, or supplier record.

• Reduces misfiled documents and duplicate uploads
• Improves searchability across product development records
• Creates a more reliable single source of truth

7. Exception handling for document-driven workflow approvals

Data flow: Google Document AI to DeSL

When Google Document AI detects missing fields, inconsistent values, or unreadable sections in a document, DeSL can route the related workflow to the appropriate team for review. For example, a missing composition percentage in a material certificate can trigger a task for the sourcing or compliance team before the item advances.

• Prevents downstream errors in product development
• Creates clear exception workflows for operational teams
• Improves governance over critical document approvals

8. Historical document digitization for legacy product and supplier records

Data flow: Google Document AI to DeSL

Legacy archives such as scanned product files, old supplier agreements, and historical compliance documents can be digitized with Google Document AI and loaded into DeSL. This makes older records searchable and usable within current PLM and supply chain workflows, which is especially valuable during audits, product reissues, or supplier disputes.

• Converts archived paper records into usable digital assets
• Improves access to historical product and sourcing information
• Supports audit, traceability, and rework scenarios

These integrations are most valuable when DeSL remains the system of record for product and supply chain processes, while Google Document AI acts as the document intelligence layer that converts unstructured content into actionable data.