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Multimodal Workflows

Combine multiple Universal AI features to build powerful processing pipelines.

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Overview

Universal AI’s multimodal workflows enable you to:
  • Chain multiple AI features together
  • Process files through multiple stages
  • Combine text, image, and document analysis
  • Build complex automation pipelines
Unlike LLM multimodal (which focuses on conversational understanding of media), Universal AI multimodal workflows are about processing pipelines and structured data extraction.

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Workflow Patterns

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Sequential Processing

Process data through multiple features in sequence: 
Input File → OCR → Translation → Summary
Image → Detection → Classification → Analysis

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Parallel Processing

Run multiple features simultaneously: 
Document → [OCR, Identity Parser, Invoice Parser] → Aggregate Results
Image → [Object Detection, Face Detection, Explicit Content] → Combined Analysis

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Conditional Routing

Route to different features based on results: 
Image → Moderation → [If safe: Generate Caption | If unsafe: Flag for Review]
Text → AI Detection → [If AI: Analyze Further | If Human: Skip]

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Common Multimodal Workflows

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Document Processing Pipeline

Extract, translate, and analyze documents:

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Image Content Moderation Pipeline

Screen images through multiple checks:

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Invoice Processing Workflow

Extract and validate invoice data:

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Multilingual Content Pipeline

Process content in multiple languages:

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Advanced Patterns

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Parallel Feature Execution

Run multiple features simultaneously for faster processing:

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Conditional Workflows

Route based on analysis results:

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Iterative Refinement

Refine results through multiple passes:

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Real-World Workflow Examples

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E-commerce Product Processing

Process product images and descriptions:

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Content Moderation Pipeline

Comprehensive content screening:

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Best Practices

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Error Handling in Workflows

Handle failures gracefully:

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Cost Optimization

Monitor and optimize workflow costs:

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Next Steps