BRIA AI - OpenText Decision Service Integration and Automation

Common Integration Use Cases Between BRIA AI and OpenText Decision Service

1. Rule-Governed Product Image Generation for E-commerce Catalogs

Data flow: OpenText Decision Service ? BRIA AI

OpenText Decision Service can evaluate product attributes such as category, region, price tier, season, and channel requirements, then determine the correct image style, background, and compliance rules for each product. BRIA AI uses those decision outputs to generate approved product visuals at scale.

• Automatically selects image variants for marketplace, web, and mobile channels
• Applies region-specific visual rules, such as cultural context or local promotions
• Reduces manual creative review for routine catalog updates

Business value: Faster product launches, more consistent catalog imagery, and fewer errors in channel-specific content.

2. Automated Creative Approval Routing Based on Content Risk

Data flow: BRIA AI ? OpenText Decision Service

BRIA AI generates or edits images, then sends metadata such as content type, detected objects, brand elements, and intended use to OpenText Decision Service. The decision engine applies business rules to determine whether the asset can be auto-approved, requires legal review, or must be rejected.

• Routes high-risk assets to brand, legal, or compliance teams
• Auto-approves low-risk variations for routine campaigns
• Enforces policies for regulated industries, claims, or restricted imagery

Business value: Shorter approval cycles, stronger governance, and reduced compliance exposure.

3. Market-Specific Campaign Personalization

Data flow: OpenText Decision Service ? BRIA AI

OpenText Decision Service can determine which creative version should be produced based on customer segment, geography, language, seasonality, and campaign objective. BRIA AI then generates localized image variants tailored to those decision outcomes.

• Creates different visuals for premium versus value segments
• Adjusts imagery for regional preferences and seasonal promotions
• Supports A/B testing with rule-selected creative variants

Business value: Higher campaign relevance, improved conversion rates, and more efficient localization.

4. Dynamic Asset Selection for Omnichannel Publishing

Data flow: Bi-directional

OpenText Decision Service can decide which BRIA AI-generated asset should be used for each channel based on format, audience, campaign priority, and performance rules. BRIA AI supplies multiple image options, while the decision service selects the best fit for each publishing target.

• Chooses the right image ratio and style for social, email, web, and marketplace channels
• Uses performance rules to prioritize higher-converting visuals
• Supports automated content assembly in downstream publishing tools

Business value: Better channel consistency, less manual asset coordination, and improved content performance.

5. Exception Handling for Restricted or Sensitive Visual Content

Data flow: BRIA AI ? OpenText Decision Service

When BRIA AI generates imagery involving sensitive products, regulated claims, or potentially restricted contexts, the asset metadata can be evaluated by OpenText Decision Service. The rules engine determines whether additional checks are needed before the content can be used.

• Flags assets containing regulated product categories
• Applies special handling for age-restricted or region-restricted content
• Triggers escalation workflows for exceptions

Business value: Lower risk of policy violations and more reliable governance for sensitive content.

6. Automated Creative Variant Prioritization for A/B Testing

Data flow: OpenText Decision Service ? BRIA AI

Marketing teams can define rules in OpenText Decision Service to determine how many variants to generate, which audience segments to test, and which visual attributes to vary. BRIA AI then produces the required image set for experimentation.

• Generates controlled variations for headlines, backgrounds, product placement, and lifestyle context
• Aligns creative output with test design rules
• Supports rapid experimentation without manual design bottlenecks

Business value: Faster test execution, more disciplined experimentation, and better creative optimization.

7. Brand Policy Enforcement for AI-Generated Assets

Data flow: OpenText Decision Service ? BRIA AI

OpenText Decision Service can encode brand standards such as approved colors, background types, product positioning, and prohibited visual elements. BRIA AI consumes those rules during generation so the output stays aligned with brand policy from the start.

• Prevents generation of off-brand imagery
• Standardizes visual output across teams and regions
• Reduces rework caused by brand noncompliance

Business value: Stronger brand consistency, fewer revisions, and more scalable creative operations.

8. Workflow-Driven Creative Operations for High-Volume Content Production

Data flow: Bi-directional

OpenText Decision Service can orchestrate decisions across the creative workflow, such as when to generate new assets, when to reuse existing ones, and when to escalate for human review. BRIA AI provides the visual generation and editing capability within that workflow.

• Determines whether an existing asset can be adapted instead of recreated
• Routes tasks based on campaign urgency, asset type, and approval thresholds
• Improves coordination between marketing, creative, and compliance teams

Business value: Lower production cost, better throughput, and more predictable content delivery.