CELUM - Google Document AI Integration and Automation

Common Integration Use Cases Between CELUM and Google Document AI

1. Automated ingestion of scanned contracts and legal documents into CELUM

Data flow: Google Document AI ? CELUM

Google Document AI can extract text, key fields, and document classifications from scanned contracts, NDAs, supplier agreements, and other legal files. The extracted metadata can then be pushed into CELUM to store the documents with the correct naming, tags, approval status, and retention attributes. This reduces manual indexing effort and makes sensitive documents easier to search, govern, and retrieve across legal and procurement teams.

2. Metadata enrichment for marketing and brand asset libraries

Data flow: Google Document AI ? CELUM

When marketing teams receive source files such as product sheets, packaging proofs, or campaign briefs in PDF or image format, Google Document AI can extract product names, dates, regions, and other structured data. CELUM can use this information to automatically enrich asset records, improving asset discoverability and supporting better version control. This is especially valuable for global teams managing large volumes of multilingual content.

3. Automated rights and compliance review for externally sourced content

Data flow: Google Document AI ? CELUM

Organizations often receive third-party content such as talent releases, usage licenses, or vendor agreements in scanned form. Google Document AI can extract expiration dates, usage terms, and named entities, then pass the results to CELUM for rights management and compliance workflows. CELUM can flag assets with expiring usage rights, trigger review tasks, and prevent unauthorized publishing of content with restricted terms.

4. Conversion of inbound customer or partner documents into governed content assets

Data flow: Google Document AI ? CELUM

Sales, partner, and customer-facing teams frequently receive forms, statements, and supporting documents that need to be stored centrally for collaboration. Google Document AI can classify and extract data from these documents, while CELUM can archive the original files alongside structured metadata and workflow status. This creates a controlled repository for downstream teams such as operations, customer service, and account management.

5. Document-driven campaign content preparation

Data flow: Google Document AI ? CELUM

Campaign teams often work from source documents such as creative briefs, product specifications, and regulatory disclosures. Google Document AI can extract key requirements and content elements from these documents and feed them into CELUM as reference metadata or linked assets. This helps content managers assemble campaign packages faster and reduces the risk of missing mandatory statements, disclaimers, or regional variations.

6. Automated extraction of product and packaging information for content operations

Data flow: Google Document AI ? CELUM

For manufacturers and retailers, product documentation often arrives as PDFs, scans, or supplier submissions. Google Document AI can extract product identifiers, dimensions, ingredients, compliance statements, and language variants. CELUM can then store these documents with accurate metadata, making it easier for brand, regulatory, and localization teams to manage approved content versions and reuse them across channels.

7. Bi-directional workflow for document review and approval

Data flow: CELUM ? Google Document AI

CELUM can send newly uploaded documents to Google Document AI for extraction and classification, then receive the structured output back for review and approval routing. Once approved, CELUM can publish the finalized asset package to downstream systems such as CMS, PIM, or creative tools. This bi-directional flow supports a more efficient content governance process by combining automated document understanding with CELUM?s approval and distribution capabilities.

8. Search optimization for archived document repositories

Data flow: Google Document AI ? CELUM

Large organizations often have legacy document archives with limited metadata. Google Document AI can process these files in bulk, extract searchable text and document attributes, and update CELUM records accordingly. This improves enterprise search, reduces time spent locating historical documents, and helps teams reuse approved content instead of recreating it.