You will get A production backend infrastructure for your AI-powered automation
Project details
Most "AI integrations" are just API wrappers — a single ChatGPT call with no retry logic, no rate limiting, no structured output handling, and no observability. They work in demos and break in production.
I build the infrastructure layer that makes LLM-powered systems production-ready. Async pipelines, token management, rate limiting, structured output with Pydantic, PostgreSQL logging of every inference, and graceful fallback when the model fails or quota is hit.
I've built a production AI pipeline using Groq for lead scoring connected to an autonomous WhatsApp outreach agent — handling conversation state, follow-up scheduling, and response classification at scale. The code is public on GitHub.
What you get: a backend that treats the LLM as one component in a reliable system — not the entire system. Clean async Python, Docker setup, full observability, and code you can maintain and extend.
I build the infrastructure layer that makes LLM-powered systems production-ready. Async pipelines, token management, rate limiting, structured output with Pydantic, PostgreSQL logging of every inference, and graceful fallback when the model fails or quota is hit.
I've built a production AI pipeline using Groq for lead scoring connected to an autonomous WhatsApp outreach agent — handling conversation state, follow-up scheduling, and response classification at scale. The code is public on GitHub.
What you get: a backend that treats the LLM as one component in a reliable system — not the entire system. Clean async Python, Docker setup, full observability, and code you can maintain and extend.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Classification, AI-Generated Art, AI-Generated Code, AI-Generated Video, Conversational AIAI Development Language
PythonAI Models
ChatGPT, GPT-4, LLaMA, Midjourney AI, OpenAI Codex, WhisperWhat's included $1,200
These options are included with the project scope.
$1,200
- Delivery Time 4 days
- Number of Revisions 1
- AI Model Integration
- Database Integration
- Detailed Code Comments
Optional add-ons
You can add these on the next page.
Additional Revision
+$80
autonomous agent with conversation state
+$200
WhatsApp/email action layer
+$200
token usage dashboard + cost tracking
+$200About Mohamed
Python Automation Engineer, Scraping, Lead Generation & Bot Developme
Sakiet ed Daier, Tunisia - 6:20 pm local time
Lead Sourcer, my main project, is a fully automated lead generation pipeline — it finds business owners, qualifies them, and handles outreach without a human touching anything between start and delivery. Built in Python with async scraping, Redis, PostgreSQL, and zero paid APIs.
That's the kind of work I do: you have a repetitive process that eats hours every week, I turn it into something that runs on a server and sends you results.
Specific things I've shipped: multi-source scraping engines with proxy rotation and anti-detection, Telegram and WhatsApp bots with state machine logic, data pipelines that sync across Google Sheets, databases, and third-party APIs, and VPS deployments that stay running.
If you have a process that's manual, slow, and predictable — it can probably be automated. That's what I'm here for.
Steps for completing your project
After purchasing the project, send requirements so Mohamed can start the project.
Delivery time starts when Mohamed receives requirements from you.
Mohamed works on your project following the steps below.
Revisions may occur after the delivery date.
Client setup assistance
We assist clients till they finally set up everything clearly even though the project will be fully documented