Azure Computer Vision - Agility Integration and Automation

Common Integration Use Cases Between Azure Computer Vision and Agility

1. Automated image tagging and metadata enrichment for Agility content libraries

Data flow: Azure Computer Vision → Agility

When marketers or content editors upload images into Agility, Azure Computer Vision can automatically detect objects, scenes, brands, and text, then push structured metadata back into Agility fields or associated asset records. This reduces manual tagging effort and improves searchability across large content libraries.

• Speeds up content publishing by removing manual asset classification
• Improves internal search and reuse of approved images
• Supports consistent metadata standards across teams and regions

2. OCR extraction from scanned documents and image-based content for web publishing

Data flow: Azure Computer Vision → Agility

Agility can receive extracted text from scanned PDFs, screenshots, forms, or image-based documents processed by Azure Computer Vision OCR. The extracted content can be stored in Agility as page copy, searchable text, or structured content fields for reuse across websites and landing pages.

• Reduces manual transcription work for marketing and operations teams
• Makes image-based content searchable and indexable
• Improves accessibility and content repurposing across channels

3. Automated alt text generation for accessibility compliance

Data flow: Azure Computer Vision → Agility

Azure Computer Vision can generate descriptive text for images uploaded into Agility, which can then be mapped to alt text fields in the CMS. This helps content teams publish accessible web experiences faster while maintaining compliance with accessibility standards.

• Supports WCAG-aligned publishing workflows
• Reduces dependency on editors to manually write alt text for every asset
• Improves accessibility coverage at scale for high-volume content operations

4. Smart content moderation and brand safety review before publishing

Data flow: Azure Computer Vision → Agility

Before an image is approved for use in Agility, Azure Computer Vision can analyze it for unsafe, inappropriate, or off-brand content such as explicit imagery, unexpected objects, or unapproved logos. The CMS can then route flagged assets into a review workflow for legal, compliance, or brand teams.

• Reduces reputational risk from publishing unsuitable visuals
• Creates a controlled review process for regulated or brand-sensitive organizations
• Helps marketing teams move faster with automated pre-screening

5. Product image classification for content-rich commerce pages

Data flow: Azure Computer Vision → Agility

For organizations using Agility to manage product landing pages, Azure Computer Vision can identify product categories, attributes, and visual characteristics from uploaded images. That data can be used to auto-populate content fields, recommend related assets, or support dynamic page assembly for commerce experiences.

• Improves consistency in product page creation
• Accelerates merchandising workflows for large catalogs
• Helps teams reuse product imagery more effectively across campaigns and channels

6. Visual asset discovery and intelligent search inside Agility

Data flow: Azure Computer Vision → Agility

Azure Computer Vision can enrich assets with tags such as people, locations, objects, and text, enabling more precise search and filtering within Agility. Editors can quickly find the right image or video frame without relying on file names or manual folder structures.

• Reduces time spent searching for approved assets
• Improves content reuse across campaigns, microsites, and regional pages
• Supports better governance for large distributed content teams

7. Editorial workflow automation for image review and approval

Data flow: Bi-directional

Agility can trigger Azure Computer Vision analysis when a new asset is added or updated, then use the results to route content through different approval paths. For example, an image containing text, a logo, or a person may require additional legal or brand review before publication.

• Creates rule-based workflows tied to asset characteristics
• Improves governance without slowing down routine publishing
• Helps separate low-risk and high-risk content for faster approvals

8. Automated content enrichment for multi-channel publishing

Data flow: Azure Computer Vision → Agility

Agility can use Azure Computer Vision outputs to enrich content objects that are published across websites, mobile apps, and campaign landing pages. For example, a single image upload can generate tags, alt text, OCR text, and moderation status, all stored centrally in Agility for downstream channel delivery.

• Ensures consistent asset data across all digital touchpoints
• Reduces duplicate work for web, mobile, and campaign teams
• Improves speed and quality of omnichannel content operations