Brandana: Participating at the Nanobanana Hackathon

The Challenge of Brand Asset Generation

Creating consistent, high-quality marketing assets is time-consuming and expensive. Designers spend hours creating social media graphics, marketing banners, and brand illustrations that align with brand guidelines. What if AI could automate this process while maintaining brand consistency?

Introducing Brandana

Brandana is an AI-powered platform built during the Nanobanana hackathon that automates the generation of marketing assets. By combining brand analysis with cutting-edge AI image generation, Brandana transforms brand inputs into production-ready visual assets.

Think of it as having an AI design assistant that understands your brand identity and creates custom graphics on demand.

The Technology Stack

Brandana leverages two powerful AI technologies to deliver intelligent asset generation:

1) Google Gemini for Brand Analysis

The Gemini API analyzes brand inputs and creates optimized prompts for image generation:

• Brand understanding: Processes brand guidelines, color schemes, and style preferences
• Prompt engineering: Generates detailed, context-aware prompts optimized for visual consistency
• Style translation: Converts brand requirements into AI-friendly descriptions

2) FAL.ai for Image Generation

FAL (Fast AI Labs) powers the actual image creation:

• High-quality generation: Creates production-ready marketing assets
• Fast processing: Optimized for speed without sacrificing quality
• Flexible output: Supports various aspect ratios and image formats

Architecture Overview

The platform follows a clean, pipeline-based architecture:

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│  Brand Input    │───▶│  Gemini Prompt   │───▶│   FAL Image     │
│  (Guidelines)   │    │   Generator      │    │   Generation    │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │                         │
                              ▼                         ▼
                       ┌──────────────┐        ┌──────────────┐
                       │  Optimized   │        │  Generated   │
                       │   Prompts    │        │    Assets    │
                       └──────────────┘        └──────────────┘

Core Components:

Brand Input Layer

• Accepts brand guidelines, style preferences, and asset requirements
• Processes color palettes, typography rules, and visual identity
• Defines output specifications (dimensions, format, use case)

Prompt Generation Engine (Gemini)

• Analyzes brand characteristics and asset requirements
• Constructs detailed, optimized prompts for image generation
• Ensures consistency with brand identity
• Adapts prompts based on asset type (social media, banner, illustration)

Image Generation Pipeline (FAL)

• Receives optimized prompts from Gemini
• Generates high-quality visual assets
• Applies brand-specific styling and constraints
• Outputs production-ready images

Asset Management

• Stores generated assets with metadata
• Tracks generation parameters for reproducibility
• Enables batch processing and variation generation

The Workflow

Step 1: Brand Analysis

Users provide brand information:

• Brand guidelines and visual identity documents
• Color schemes and typography preferences
• Style references and mood boards
• Target asset types (social posts, banners, illustrations)

Step 2: Intelligent Prompt Generation

Gemini processes the brand input:

• Analyzes brand characteristics and visual language
• Identifies key styling elements and constraints
• Generates optimized prompts that capture brand essence
• Tailors prompts for specific asset requirements

Step 3: Asset Generation

FAL creates the visual assets:

• Processes Gemini-generated prompts
• Applies brand-specific styling
• Generates multiple variations if needed
• Outputs high-resolution, production-ready images

Step 4: Delivery

Generated assets are ready to use:

• Download in various formats and sizes
• Maintain consistency across asset types
• Iterate quickly with new variations
• Scale asset production efficiently

Results: Production-Ready Assets

Brandana generates three primary types of marketing assets:

Social Media Graphics

• Platform-optimized dimensions (Instagram, Twitter, LinkedIn)
• Brand-consistent color schemes and typography
• Engaging visuals that match brand identity
• Ready for immediate posting

Marketing Banners

• Web and display advertising formats
• Attention-grabbing designs aligned with brand
• Multiple size variations from single input
• Professional quality suitable for campaigns

Brand Illustrations

• Custom illustrations matching brand style
• Scalable vector-style outputs
• Unique visuals that reinforce brand identity
• Versatile assets for various marketing needs

The Nanobanana Hackathon Experience

Building Brandana during the hackathon showcased the power of combining specialized AI services:

Key Learnings:

• API orchestration: Successfully chained Gemini and FAL APIs for intelligent workflows
• Prompt engineering: Learned how AI-generated prompts can improve image quality
• Brand consistency: Discovered techniques for maintaining visual consistency across generated assets
• Rapid prototyping: Built a functional pipeline in hackathon timeframe

Technical Achievements:

• Integrated two different AI platforms seamlessly
• Created a reproducible asset generation pipeline
• Achieved brand-consistent outputs across asset types
• Demonstrated practical AI application for marketing

Use Cases

Marketing Teams

• Campaign assets: Generate complete asset sets for campaigns
• Social media content: Create consistent social graphics at scale
• A/B testing: Quickly produce variations for testing
• Event marketing: Rapid asset creation for time-sensitive events

Startups and Small Businesses

• Cost-effective design: Generate professional assets without design team
• Brand consistency: Maintain visual identity across channels
• Speed to market: Launch campaigns faster with automated asset creation
• Scaling visual content: Grow content production as business scales

Agencies

• Client assets: Produce initial concepts and variations quickly
• Pitches and proposals: Create mockups for client presentations
• Production efficiency: Augment design team output
• Rapid iteration: Test multiple creative directions efficiently

Future Directions

The Brandana prototype demonstrates significant potential for expansion:

Enhanced Brand Learning

• Train custom models on specific brand assets
• Build brand-specific style profiles
• Improve consistency through fine-tuning

Expanded Asset Types

• Video thumbnails and motion graphics
• Email marketing templates
• Presentation slide designs
• Print materials and packaging

Workflow Integration

• Direct integration with design tools (Figma, Canva)
• Publishing to social media platforms
• Asset management and versioning
• Team collaboration features

Advanced Customization

• Manual prompt refinement options
• Style transfer from reference images
• Multi-language support for global brands
• Custom model training for enterprise clients

The Future of Brand Asset Creation

Brandana represents a shift in how brands can approach visual content creation. By combining brand intelligence (Gemini) with generative capabilities (FAL), we can automate repetitive design work while maintaining the creative consistency that brands require.

The hackathon proved that AI-powered asset generation is not just possible, it is practical, fast, and capable of producing production-quality results.

Credits: Built during the Nanobanana Hackathon. Powered by Google Gemini and FAL.ai