You will get a custom RAG AI assistant grounded in your business documents
Project details
Turn your documents and business knowledge into an AI assistant that can provide relevant, grounded answers instead of relying only on generic model knowledge.
I will build a custom RAG solution around your use case, whether you need an internal knowledge assistant, document Q&A system, support tool, research assistant, or an AI feature for an existing application.
Depending on your selected package, the project can include document processing, retrieval and vector search, LLM integration, citations, APIs, databases, a user interface, Docker setup, testing, and deployment support.
I work across the complete system not only the AI prompt so the final solution can connect properly with your existing data, software, and workflows.
We will confirm the exact architecture, document sources, integrations, and expected user experience during the project kickoff.
Third-party API, model, hosting, and external service usage fees are not included unless explicitly agreed.
I will build a custom RAG solution around your use case, whether you need an internal knowledge assistant, document Q&A system, support tool, research assistant, or an AI feature for an existing application.
Depending on your selected package, the project can include document processing, retrieval and vector search, LLM integration, citations, APIs, databases, a user interface, Docker setup, testing, and deployment support.
I work across the complete system not only the AI prompt so the final solution can connect properly with your existing data, software, and workflows.
We will confirm the exact architecture, document sources, integrations, and expected user experience during the project kickoff.
Third-party API, model, hosting, and external service usage fees are not included unless explicitly agreed.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Gradio, Hugging Face, StreamlitAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$100
|
Standard
$250
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 8 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | |
Model Documentation | - | ||
Model Monitoring | - | - | - |
Model Testing & Optimization | |||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Optional add-ons
You can add these on the next page.
Cloud/VPS Deployment
(+ 1 Day)
+$75
Additional API Integration
(+ 1 Day)
+$25
Simple Web Interface
(+ 2 Days)
+$75Frequently asked questions
About Mohamed Habib
AI Automation & Agent Developer | RAG, Voice AI, LangChain, Python
Ben Arous, Tunisia - 11:34 pm local time
🤖 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 • 𝗥𝗔𝗚 • 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜 • 𝗟𝗲𝗮𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗦𝗮𝗹𝗲𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻
🌐 𝗣𝘆𝘁𝗵𝗼𝗻 • 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 • 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 • 𝗔𝗣𝗜𝘀 • 𝗗𝗼𝗰𝗸𝗲𝗿
I build production-oriented AI agents, RAG systems, Voice AI applications, AI-powered lead generation and sales automation systems, intelligent automation workflows, and AI-powered SaaS products that connect your data, APIs, and business processes.
My focus is not just creating AI demos. I build complete systems from architecture and backend development to AI integration, databases, automation, testing, and deployment.
🟢 𝗡𝗲𝘄 𝘁𝗼 𝗨𝗽𝘄𝗼𝗿𝗸 | 𝗡𝗼𝘁 𝗡𝗲𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴
I recently joined Upwork, but I already have hands-on experience designing and developing real AI products and automation systems. You can explore my portfolio to see examples of the platforms, AI workflows, and applications I have built.
My goal with every project is simple: understand the actual business problem, choose the right architecture, communicate clearly, and deliver a reliable solution that can be used beyond a prototype.
🤖 𝗪𝗵𝗮𝘁 𝗜 𝗖𝗮𝗻 𝗕𝘂𝗶𝗹𝗱
🔹 AI Agents & Agentic Workflows
Custom AI agents that reason, use tools, call APIs, interact with databases, and automate multi-step business processes.
🔹 RAG & Knowledge Assistants
AI systems grounded in your private documents, PDFs, databases, or company knowledge using embeddings, vector search, retrieval, and contextual generation.
🔹 AI Lead Generation & Sales Automation
AI-powered workflows for B2B prospect research, lead enrichment, ICP-based qualification, lead scoring, personalized outreach, CRM automation, follow-ups, and sales pipeline workflows.
🔹 Voice AI Applications
Real-time and asynchronous Voice AI systems combining Speech-to-Text, LLM reasoning, tool/API integrations, and Text-to-Speech.
🔹 AI Automation
Intelligent workflows for content generation, lead generation, sales operations, CRM processes, prospect qualification, data processing, API integration, internal operations, publishing, and repetitive business processes.
🔹 LLM Integrations
OpenAI, GPT, Claude, structured outputs, function/tool calling, prompt systems, contextual conversations, and AI-powered application features.
🔹 AI Evaluation Systems
Response scoring, quality evaluation, structured feedback, benchmarking, and performance analysis for LLM-powered applications.
🔹 Computer Vision & Industrial AI
Classification, product recognition, product detection, SKU/data extraction, and AI integration into operational workflows.
🔹 AI SaaS & Full-Stack Applications
Complete AI-powered products with backend APIs, frontend interfaces, authentication, databases, automation, and deployment.
🖥️ 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝘁𝗮𝗰𝗸
AI & LLMs:
AI Agents • RAG • LangChain • LLM APIs • OpenAI • Claude • Prompt Engineering • Embeddings • Vector Search • Voice AI • STT • TTS
Backend:
Python • FastAPI • Django • Node.js • REST APIs • API Integrations
Sales & Business Automation:
B2B Lead Generation • Lead Qualification • Lead Scoring • CRM Automation • Sales Automation
Frontend:
React • Next.js • TypeScript • JavaScript • Tailwind CSS
Data:
PostgreSQL • MongoDB • Redis • Vector Databases
Infrastructure:
Docker • Cloud/VPS Deployment • Background Workers • API-Based Architectures
🚀 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
📺 TeleMedia | AI Social Media Automation SaaS
Built an AI-powered content and publishing platform combining automated content generation, subtitle generation, topic/title generation, TTS, media processing, scheduling, API integrations, and automated publishing workflows.
🩺 FSP Praxis | RAG-Powered Medical Training Platform
Developed an AI medical training system where users practice structured conversations with a knowledge-grounded AI system. Built RAG-based knowledge retrieval together with evaluation, scoring, and structured feedback mechanisms.
🏭 Industrial AI & Business Platform
Developed an industrial web and AI solution integrating product classification, detection, SKU extraction, structured data processing, and AI directly into business workflows.
⭐ 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗘𝘅𝗽𝗲𝗰𝘁
✔ Production-oriented architecture
✔ Clean Python/API development
✔ AI and third-party API integrations
✔ Database and backend implementation
✔ Dockerized deployment when appropriate
✔ Testing and debugging
✔ Clear milestones and communication
✔ Documentation and project handover
I’m especially interested in projects involving AI Agents, RAG, Voice AI, AI Automation, LLM integrations, and AI SaaS products.
If you already know what you want to build, send me the requirements. If you only have the business problem, I can also help define the AI architecture and determine the most practical way to build it.
🚀 Let’s turn your AI idea or manual workflow into a working system.
Steps for completing your project
After purchasing the project, send requirements so Mohamed Habib can start the project.
Delivery time starts when Mohamed Habib receives requirements from you.
Mohamed Habib works on your project following the steps below.
Revisions may occur after the delivery date.
Project review & kickoff
I’ll review your requirements and we’ll confirm the use case, scope, and best implementation approach.
RAG architecture & setup
I’ll prepare the document, retrieval, AI model, and application structure for your use case.