Steg.ai - Google Document AI Integration and Automation

Common Integration Use Cases Between Steg.ai and Google Document AI

1. Secure intake and classification of scanned business documents

Data flow: Google Document AI ? Steg.ai

When invoices, contracts, claims, or onboarding forms are processed in Google Document AI, the extracted document type, metadata, and confidence scores can be sent to Steg.ai to apply content protection and asset tagging rules. This helps organizations automatically classify sensitive documents and apply the right handling policy based on document category.

• Reduces manual sorting of incoming documents
• Improves governance for sensitive records
• Supports faster downstream routing to legal, finance, or operations teams

2. Automated protection of extracted document assets

Data flow: Google Document AI ? Steg.ai

After Google Document AI extracts text, tables, or embedded images from a document, Steg.ai can be used to identify and protect sensitive visual content such as signatures, IDs, logos, or confidential diagrams. This is useful for organizations that need to share processed documents while controlling exposure of high-risk content.

• Applies protection workflows to sensitive visual elements
• Helps prevent unauthorized reuse of extracted assets
• Supports compliance requirements for regulated content

3. Enriched metadata for searchable document repositories

Data flow: Google Document AI ? Steg.ai ? enterprise content repository or DAM

Google Document AI can extract structured metadata from documents, while Steg.ai can add image-based tags and protection labels. Together, they create richer metadata for document repositories, making it easier for teams to search, filter, and govern content across shared systems.

• Improves findability of documents and associated assets
• Creates more complete metadata for records management
• Supports legal, procurement, and compliance search use cases

4. Contract and policy document review with sensitive image detection

Data flow: Google Document AI ? Steg.ai

Legal and compliance teams often process contracts, policy documents, and agreements that include embedded signatures, stamps, seals, or branded visuals. Google Document AI can extract the text and structure, while Steg.ai can detect and tag these embedded visual elements for review or protection.

• Speeds up contract intake and review workflows
• Identifies visual elements that may require special handling
• Helps standardize approval and archival processes

5. Claims and case file processing with evidence asset tagging

Data flow: Google Document AI ? Steg.ai

In insurance, healthcare, or public sector workflows, Google Document AI can extract information from claim forms, case files, and supporting documents. Steg.ai can then tag related images or attachments, such as photos, scans, or supporting evidence, so claims teams can quickly locate and protect the right assets.

• Improves case file organization
• Reduces time spent manually identifying supporting evidence
• Supports auditability and chain-of-custody needs

6. Automated redaction and controlled sharing of sensitive documents

Data flow: Bi-directional

Google Document AI can identify text and document structure, while Steg.ai can classify and protect sensitive visual content. Together, they can support a workflow where documents are analyzed, sensitive elements are flagged, and protected versions are prepared for sharing with external parties, partners, or internal teams with limited access.

• Enables safer document distribution
• Reduces manual redaction effort
• Improves consistency in privacy and security handling

7. Intelligent onboarding document processing

Data flow: Google Document AI ? Steg.ai

For HR, vendor onboarding, or customer onboarding processes, Google Document AI can extract data from forms, IDs, and supporting documents. Steg.ai can then tag and protect the associated images and document assets, helping organizations maintain secure records while accelerating onboarding workflows.

• Shortens onboarding cycle times
• Improves document compliance and retention controls
• Supports centralized management of sensitive identity documents

8. Cross-system governance for digital archives and records

Data flow: Google Document AI ? Steg.ai ? records management or DAM platform

Organizations with large digital archives can use Google Document AI to extract document metadata at scale and Steg.ai to classify and protect the associated assets. This creates a more governed records environment where archives are easier to audit, retain, and retrieve according to business and regulatory requirements.

• Enhances records governance across departments
• Improves audit readiness and retention management
• Reduces manual metadata entry for archived content