AdForge AI - Enterprise Visual Production Platform

This is a submission for the DEV's Worldwide Show and Tell Challenge Presented by Mux

What I Built

AdForge AI is a complete enterprise visual production platform that transforms brand guidelines and campaign briefs into production-ready marketing assets at scale using an automated multi-agent AI pipeline.

Marketing teams spend weeks creating campaign visuals—coordinating designers, waiting for revisions, losing brand consistency across platforms. AdForge AI solves this with deterministic, controllable, and reproducible visual generation.

Key Capabilities:

• 🎨 AI-powered brand DNA extraction from minimal input
• 🚀 Automated multi-agent pipeline for asset generation
• 🎛️ JSON-native visual controls (camera, lighting, composition)
• 🔄 Natural language refinement without full regeneration
• 🛡️ Deterministic reproducibility with seeds
• 📤 Multi-destination export (PDF, Slack, HDR)

My Pitch Video

Demo

🔗 Live Demo: https://ad-forge-ai.vercel.app
🔗 GitHub: https://github.com/omkardongre/AdForge-AI

No login required - fully accessible for testing.

The Story Behind It

As a developer working with marketing teams, I watched them spend weeks creating campaign visuals - coordinating between designers, waiting for revisions, and losing brand consistency across platforms. It's slow, expensive, and broken.

Most AI tools just generate random outputs. Marketing teams need deterministic, controllable, and reproducible visual generation. That's why I built AdForge AI.

What Makes It Special

1. Multi-Agent Pipeline

Instead of a single AI call, AdForge uses specialized agents working together:

• Brand DNA Extractor: Analyzes minimal input to create complete brand guidelines
• Scene Composer: Creates visual concepts from campaign briefs
• JSON Generator: Builds structured prompts with camera/lighting/composition parameters
• Variation Generator: Creates multiple versions with parameter variations
• Quality Assurance: Validates brand compliance

2. JSON-Native Visual Controls

Unlike traditional AI tools that regenerate everything from scratch, AdForge gives you direct control over generation parameters:

• Camera Settings: Angle (eye-level, low, high, bird's eye), FOV (10-180°), focal length (24mm-85mm), distance
• Lighting Settings: Setup (studio soft, dramatic, golden hour, natural), direction (front, side, back), intensity (0-2), color temperature
• Color Settings: Brand color palette integration, temperature, saturation (0-2), contrast (0-2)
• Composition Settings: Rule of thirds, depth of field, framing, negative space

Camera angle, lighting setup, and light direction use dropdown selectors. Saturation and contrast use visual sliders. All parameters map to structured JSON sent to the generation API.

3. Deterministic Reproducibility

Every generated image has a reproducibility seed. Same seed + same JSON = identical output. Enterprises can recreate approved assets months later with pixel-perfect accuracy.

Reproducibility Proof Feature:

• Every asset displays a Shield icon showing its seed value
• Copy seed → paste in Visual Controls → Regenerate = identical image
• Proves deterministic output capability

4. Three Generation Modes

Generate Mode:

• Text-to-image with structured JSON prompts
• 5 camera presets (product, lifestyle, portrait, wide, dramatic)
• 5 lighting presets automatically applied based on scene analysis

Refine Mode:

• Iterative modifications via natural language
• Example: "make the lighting warmer" or "change camera angle to low"
• Updates only specified parameters while preserving everything else
• Pre-built instruction examples in UI

Inspire Mode (Vision AI-Powered):

• Upload reference image OR enter creative direction OR both
• Gemini Vision AI analyzes reference images to extract:
  • Subject attributes, lighting conditions, shadows
  • Composition, color scheme, mood/atmosphere
  • Camera angle, depth of field, focal length
• Generates new images inspired by reference style

5. AI-Powered Campaign Analysis

Select any two campaign assets for side-by-side comparison. Gemini AI analyzes both with full campaign context:

• Brand alignment assessment
• Campaign objective fit
• Technical quality scores (1-10)
• Lighting, composition, color/mood comparison
• Winner recommendation with reasoning

Core Features

Dashboard

Real-time statistics showing total brands, active campaigns, and generated assets. Quick Actions for creating brands, campaigns, and viewing assets.

4-Step Brand Wizard

Guided workflow (Brand Info → Colors → Visual Style → Review):

• Enter minimal brand information
• Pick 3 brand colors - AI expands into complete palette (primary, secondary, background, text)
• Define visual style preferences
• Review and confirm brand DNA
• LLM expands minimal input into complete brand guidelines

Campaign Creation

• Select existing brand
• Set campaign name, objective (awareness/conversion), key message, call-to-action
• Choose target platforms: Instagram Feed/Story, Facebook Feed, LinkedIn Post, Google Display
• Configure generation settings (scenes count, variations per scene)

Multi-Platform Generation

Automatically generates assets at correct dimensions for each selected platform:

• Instagram: 1080×1080 (Feed), 1080×1920 (Story)
• LinkedIn: 1200×627
• Facebook: 1200×630
• Google Display: Various standard sizes

Asset Gallery

• Grid view of all generated assets
• Quality scores for brand compliance
• Platform dimension badges
• Action buttons for each asset (Refine, Compare, Download, Export)

Export Panel

Multi-Destination Export:

• PDF Storyboard: Professional PDF document with all campaign assets
• Slack Notifications: Team alerts when exporting assets

Print-Ready Format Export:

• TIFF: 300 DPI with enhanced sharpness and contrast for professional print
• PNG: High-quality with DPI metadata
• JPEG: 300 DPI high-quality compression

Technical Highlights

User Journey

AdForge AI follows a structured 5-stage workflow: **Onboarding* → Campaign → Generate → Refine → Export*

Backend (Python/FastAPI)

Multi-Agent Architecture:
5 specialized AI agents working in pipeline:

1. Brand DNA Extractor: Uses LLM (Gemini/OpenAI/Anthropic) to extract complete brand identity
2. Scene Composer: Creates visual scene descriptions from campaign briefs
3. JSON Generator: Builds structured prompts with camera/lighting/color/composition parameters
4. Variation Generator: Creates parameter-sweep variations
5. Quality Assurance: Validates brand compliance, calculates quality scores

Technology Stack:

• FastAPI for async/await API endpoints
• Bria API for structured JSON-based image generation
• Google Gemini for Vision AI (Inspire mode, AI Compare) and LLM orchestration
• SQLite with async support for database
• ReportLab for PDF generation
• slack_sdk for team notifications

Frontend (React/Vite/TypeScript)

UI Pages and Modals:

• Pages: Dashboard, Brands List, Brand Detail, Brand Wizard, Campaigns List, Campaign Detail, Campaign Create, Gallery, Export Panel, Settings, AI Compare, Semantic Search
• Modals: Refine Modal, Inspire Modal, Visual Controls (JSON Editor)

Key UI Components:

• 4-step Brand Wizard with color pickers and visual style selectors
• Campaign creation with brand selector and platform checkboxes
• Asset gallery with quality score badges
• Visual Controls modal with parameter sliders
• AI Compare page with side-by-side analysis
• ConsistencyBadge for reproducibility proof
• Export panel with multi-destination options

Technology Stack:

• React 18 with TypeScript
• Vite for fast development and builds
• TailwindCSS for styling
• React Router for navigation
• Axios for API communication

Challenges I Ran Into

1. JSON Schema Design: Designing the precise JSON structure for structured image generation required extensive experimentation with the Bria API's capabilities and constraints.

2. Multi-Platform Aspect Ratios: Generating consistent brand visuals across multiple platform dimensions while maintaining composition quality was challenging. Solved with platform-specific camera presets that adjust framing based on aspect ratio.

3. Vision-Based Inspire Mode: The Bria API doesn't directly accept image URLs. Integrated Gemini Vision AI to analyze reference images and build structured prompts from the visual analysis.

4. Reproducibility Implementation: Implementing seed-based deterministic generation with a user-friendly UI for copying and reusing seeds across different generation sessions.

5. Brand Consistency: Ensuring generated assets maintained brand compliance across different scenes and variations. Built the Quality Assurance agent to validate brand DNA adherence.

Accomplishments I'm Proud Of

• All 3 Generation Modes Fully Integrated: Generate, Refine, and Inspire working end-to-end with production-quality results

• JSON-Native Control System: Visual sliders for every generation parameter (camera angle, lighting, saturation, contrast) directly mapped to API parameters

• Vision AI Integration: Gemini analyzes reference images to extract visual characteristics and build generation prompts

• Campaign-Aware AI Compare: Context-aware asset comparison that considers brand guidelines and campaign objectives

• Reproducibility Proof: Every asset has a verifiable seed for exact recreation, with UI indicators

• Complete Multi-Agent Pipeline: 5 specialized agents working together seamlessly from brief to production assets

• Production Deployment: Fully deployed and working live on Vercel (frontend) and Render (backend)

• Multi-Destination Export: PDF storyboards and Slack notifications working in production

What I Learned

• Determinism is Critical for Enterprise AI: Random outputs don't work in professional workflows. Reproducible generation with seeds is essential for brand consistency and client approvals.

• Multi-Agent Systems Are Powerful: Breaking complex creative workflows into specialized agents produces better results than monolithic AI calls. Each agent can focus on its specific task with clear inputs/outputs.

• Vision AI Enables New Workflows: Gemini's image analysis unlocks reference-based generation and intelligent asset comparison, bridging the gap between traditional design workflows and AI generation.

• JSON-Native Control Changes Everything: Direct parameter control (vs. prompt engineering) is a game-changer for enterprise workflows. Designers need precise, predictable control.

• Real-World Tools Need Export: Production tools must integrate with existing workflows (PDF, Slack, print formats) to be truly useful. Generation is only half the story.

What's Next

• Real-time Collaboration: Multi-user editing with live preview and commenting
• A/B Test Analytics: Performance analytics on generated variations with engagement metrics
• DAM Integration: Connect with existing Digital Asset Management systems (e.g., Bynder, Widen)
• Batch Processing: Generate hundreds of variations in parallel for large-scale campaigns
• Enhanced Semantic Search: Natural language asset search using vector embeddings (ChromaDB)
• Version Control: Git-like versioning for brand guidelines and campaign changes
• API Access: RESTful API for programmatic asset generation

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