🚀 I recently built an AI-powered Ad Performance Monitoring & Budget Optimization SaaS Managing M...🚀 I recently built an AI-powered Ad Performance Monitoring & Budget Optimization SaaS Managing M...
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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
Niva is a premium Framer template built for AI automation agencies, AI consultants, and automation-focused teams who want to attract serious B2B and enterprise clients. It helps you showcase your services, present real outcomes, and build trust with decision-makers through a warm, considered design that stands apart from the typical AI-agency aesthetic.
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Showcase services like workflow automation, custom AI agents, lead generation, and AI strategy
Present real case studies with measurable outcomes, not vague claims
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Blog — a CMS-powered index and article template, ready for SEO content
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404, and legal pages — Privacy Policy, Terms of Service, and Cookie Policy included
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Interested in a custom version for your agency? Let's talk — DMs open.
An AI-powered phone receptionist built for real estate agencies that answers inbound calls, qualifies leads in real time, and books showings directly to Google Calendar — all within the call itself, with no manual follow-up required.
The problem: Real estate agencies routinely lose leads to missed calls — after-hours inquiries, calls during showings, or overflow during busy periods. A slow callback often means the lead has already moved on to a competitor.
What it does:
Answers every call instantly, 24/7
Naturally qualifies the caller (buy/sell intent, area of interest, showing vs. agent call)
Collects contact details and preferred timing conversationally
Checks real-time calendar availability before booking
Prevents double-bookings and duplicate entries automatically
Confirms the appointment out loud before ending the call
Built with: Retell AI (conversational voice layer), n8n (workflow automation and business logic), Google Calendar API (scheduling)
Available for: Custom builds for real estate agencies, brokerages, or property management companies looking to stop losing leads to missed calls
Built an AI-powered customer support automation workflow that can classify incoming emails, identify support requests, retrieve relevant information from a knowledge base, and assist with generating the right response.
The workflow combines AI agents, RAG, embeddings, Pinecone, Gmail, and automated notifications to reduce repetitive support work while keeping human review in the loop.
The flow includes:
📩 Incoming email detection
🤖 AI-based support request classification
📚 Knowledge base retrieval with Pinecone
🧠 Context-aware customer support agent
✍️ Automated response draft generation
🔔 Reviewer notification through Telegram
⚡ Separate handling for non-support emails
I’ve been exploring more ways to combine AI + automation + real-world business workflows, rather than using AI only as a chatbot.