AdForge AI - Enterprise Visual Production Platform

Source: Dev.to

What I Built

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

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)

Demo

• Live Demo:
• Backend API:
• GitHub:

No login required – fully accessible for testing.

The Story Behind It

As a developer working with marketing teams, I saw them spend weeks creating campaign visuals—coordinating designers, waiting for revisions, and losing brand consistency across platforms. The process is slow, expensive, and broken.

Most AI tools 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

AdForge uses specialized agents that collaborate instead of a single AI call:

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

2. JSON‑Native Visual Controls

Direct control over generation parameters without full regeneration:

• Camera angle (eye‑level, low, high, bird’s eye)
• Lighting setup (studio soft, dramatic, golden hour, natural)
• Light direction, saturation, contrast

All settings are mapped to structured JSON sent to the generation API.

3. Deterministic Reproducibility

Each image includes a reproducibility seed. The same seed + identical JSON yields an identical output, enabling enterprises to recreate approved assets months later with pixel‑perfect accuracy.

4. Three Generation Modes

• Generate: Text‑to‑image with structured prompts.
• Refine: Natural language modifications (e.g., “make lighting warmer”) that update only specific parameters.
• Inspire: Upload reference images – AI extracts visual DNA and generates new assets in that style.

5. AI‑Powered Campaign Analysis

Select any two assets and click Compare – Gemini AI analyzes both with full campaign context and provides:

• Brand alignment assessment
• Campaign fit analysis
• Data‑driven recommendation

Technical Highlights

Backend (Python / FastAPI)

• Multi‑agent architecture with async/await
• Structured image generation via Bria API
• Vision AI integration using Google Gemini
• SQLite database with async support

Frontend (React / Vite / TypeScript)

• 4‑step Brand Wizard with color pickers
• Campaign creation with multi‑platform selection
• Refine Modal, Visual Controls, Inspire Modal
• AI Compare page
• Export Panel with PDF / Slack / HDR options

Key Features Implemented

• 12 UI Pages/Modals: Dashboard, Brands (list/detail/wizard), Campaigns (list/detail/create), Gallery, Export, Settings, Compare, plus Refine, Inspire, Visual Controls, and Reproducibility Proof modals.
• Multi‑Platform Generation: Automatic asset sizing for Instagram (1080×1080), LinkedIn (1200×627), Facebook (1200×630), etc.
• Export Capabilities: PDF storyboard generation, Slack notifications, HDR formats (TIFF 300 DPI, PNG, JPEG).

What I Learned

• Determinism is critical for enterprise AI: Random outputs don’t work in professional workflows; reproducible generation with seeds is essential.
• Multi‑agent systems work: Breaking complex creative workflows into specialized agents yields better results than monolithic AI calls.
• Vision AI enables new workflows: Gemini’s image analysis unlocks reference‑based generation and intelligent asset comparison.

What’s Next

• Real‑time collaboration with live preview
• A/B test analytics on generated variations
• Digital Asset Management (DAM) integration
• Batch processing for large‑scale campaigns
• Enhanced semantic search using vector embeddings