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🚀 I recently built an AI-powered Ad Performance Monitoring & Budget Optimization SaaS
Managing Meta + Google Ads across multiple accounts often means constantly checking dashboards, comparing CPA/ROAS/CPC, and deciding where budget should move.
So I built an MVP that turns this process into an intelligent optimization workflow.
What it does:
🔗 1-Click Meta & Google OAuth
Connect ad accounts without manually copying access tokens.
📊 Automated Campaign Monitoring
Background workers continuously pull campaign performance and evaluate account health.
🚨 Anomaly & Performance Detection
Automatically detects:
• CPA/CPC/CPM spikes
• ROAS drops
• Wasted spend
• Budget concentration
• Low Google Quality Scores
• Low impression share
• Creative fatigue
🤖 Claude AI Recommendations
Instead of simply saying "Campaign X is performing badly," the system explains why and recommends where budget should potentially move.
💰 Controlled Budget Reallocation
No blind automation.
The system proposes:
Reduce → $X from Campaign A
Increase → $Y to Campaign B
The user reviews the recommendation and clicks:
👉 Approve & Execute
Only then does the platform update the campaign budgets through the advertising APIs.
🔐 Auditability & Security
Encrypted credential storage
Complete activity logs
Approval-gated execution
Multi-tenant architecture
Stripe subscription billing
Production + simulation modes
Tech Stack
Python | Django/FastAPI | PostgreSQL | Redis | Celery | Meta Graph API | Google Ads API | Anthropic Claude | OAuth 2.0 | Stripe | Docker
This project brought together several areas I enjoy working on:
Backend Engineering + APIs + AI/LLMs + Automation + SaaS Architecture
I'm particularly interested in working with SaaS founders, marketing agencies, AdTech companies, and teams looking to build AI-powered automation into their products.
If you're building something similar—or have a complex backend/API/AI automation problem—let's connect.
#Python #Django #FastAPI #AI #LLM #SaaS #AdTech #GoogleAds #MetaAds #Automation #BackendDevelopment #APIs #ClaudeAI #OpenToWork
The network for creativity
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Related posts
AI SaaS isn’t just about adding ChatGPT to a product.
The real challenge is turning AI into a useful, reliable product that solves a real business problem.
A strong AI SaaS needs:
→ A clear problem and target user
→ Simple, intuitive UX
→ Well-designed AI workflows
→ Reliable APIs and backend infrastructure
→ Proper data handling and security
→ Fast responses and scalable architecture
→ A clear path from MVP to a production-ready product
The goal isn’t to make something look “AI-powered.”
The goal is to build a product where AI genuinely makes the user’s job faster, easier, or better.
That’s what separates an AI demo from a real AI SaaS product.
Well done !
Title & Overview
Project Title: AI-Driven Ad Creatives: Gemini vs. ChatGPT Performance Test
Role: Creative VA / Graphic Designer
Tools Used: Gemini (Imagen 3), ChatGPT (DALL-E 3), Canva
The Goal (The Challenge)
In modern e-commerce marketing, high-quality product photography is vital, but traditional studio shoots are expensive and take weeks to organize. As a Creative VA, my goal was to build a rapid, zero-cost workflow to generate high-end, scroll-stopping ad concepts using generative AI.
I set up a head-to-head split test using the exact same commercial prompt across two leading AI engines to determine which platform produces superior assets for conversion-focused Meta Ads.
🛠️ The Execution & Workflow
I engineered a highly detailed marketing prompt focusing on complex luxury textures, lighting, and reflections:
"Professional studio product photography of a premium glass perfume bottle resting on a block of raw pink Himalayan salt. Surrounded by floating water droplets and wild orchids. Dramatic luxury lighting, high-end e-commerce style, hyper-realistic, square aspect ratio, 1:1 format."
My Technical Adjustments:
Forced both platforms into a 1:1 square aspect ratio to maintain symmetry for ad placement.
Analyzed how each engine managed organic elements (orchids, salt rocks) versus synthetic structures (glass perfume bottles, text typography).
📊 The Results & Insights
I published the side-by-side comparison to my professional network to test real-world user engagement. The outputs revealed massive differences in platform logic:
Gemini (Imagen 3): Excelled at hyper-realistic editorial lighting, sharp background contrast, and crisp text rendering on the product label. Best for clean, high-impact catalog ads.
ChatGPT (DALL-E 3): Mastered atmospheric styling, producing a warm, cinematic lifestyle glow and highly intricate glass detailing. Best for emotional, narrative-driven social feeds.
By mastering these specific AI engines, I cut down visual production timelines from days to minutes.
This allows my clients to rapidly A/B test premium ad variations on Meta without burning through their photography budgets.
great use of AI for faster ad testing. 👏
The client needed a better way to manage and qualify new leads without doing everything manually. I built an AI-powered system that captures new leads, qualifies them, scores them, updates the CRM, and sends the right follow-up based on each lead. The client liked how the workflow reduced manual work and made it easier to see which leads needed attention.
Tools: n8n, OpenAI, Airtable, Gmail, Webhooks
This is awesome! And segregating the leads to deal differently with each segment is so important when it comes to outreach and lead gen systems! Great work.
