You will get a RAG system that turns your documents into an AI knowledge base
Rising Talent
Project details
I will build a production RAG system that turns your documents into a searchable, conversational AI knowledge base. Your team or customers can ask questions in plain language and get accurate, cited answers pulled directly from your data.
No hallucinations. No generic responses. Every answer is grounded in your actual documents.
What you get:
· Document ingestion pipeline (PDFs, docs, web pages, databases)
· Vector database with optimized embeddings for your domain
· Hybrid search with reranking for accurate retrieval
· Chat interface or API endpoint (your choice)
· Source citations on every answer
Built with LangChain, Python, and production-grade vector databases (Pinecone, ChromaDB, or pgvector). Deployed where you need it.
I build the same for clients. You get a real, production-grade system that scales.
No hallucinations. No generic responses. Every answer is grounded in your actual documents.
What you get:
· Document ingestion pipeline (PDFs, docs, web pages, databases)
· Vector database with optimized embeddings for your domain
· Hybrid search with reranking for accurate retrieval
· Chat interface or API endpoint (your choice)
· Source citations on every answer
Built with LangChain, Python, and production-grade vector databases (Pinecone, ChromaDB, or pgvector). Deployed where you need it.
I build the same for clients. You get a real, production-grade system that scales.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language UnderstandingAI Tools
Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$499
|
Standard
$799
|
Advanced
$1,499
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 20 days |
Number of Revisions | 1 | 2 | 3 |
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.
Additional Revision
+$50Frequently asked questions
About Mehdi
AI/ML Engineer | RAG, AI Agents, LLM, Chatbots, Voice Agents, AWS
Stamford, United States - 11:55 am local time
B.S. in Computer Science. Founder of Unkommon.ai. I've built a complete AI/ML platform from the ground up (React, TypeScript, Express, PostgreSQL on AWS) and I take on select freelance projects to deliver that same production quality.
Have a project in mind but not sure where to start? Send me a message. I'm happy to do a free 30-min consultation to help you figure out the right approach.
Here's what I've built recently:
· Multi-agent voice triage and lead qualification system using LangGraph
· AI receptionist that handles customer questions, bookings, and lead capture 24/7
· Full-stack AI platform deployed on AWS (React, TypeScript, Express, PostgreSQL)
· RAG-powered knowledge base with document retrieval and conversational Q&A
· AI voice agent integrated with Telegram for autonomous task execution
· Custom business apps: loyalty programs, product catalogs, digital menus
📋 Services I Provide:
· AI chatbots and receptionists (including phone answering and voice AI)
· AI integration for existing business tools and workflows (+178% demand on Upwork)
· RAG system architecture and implementation
· Multi-agent AI system development
· AI voice agents and phone answering systems
· Custom LLM fine-tuning for industry verticals (dental, legal, real estate, finance)
· AI-powered data extraction and document processing
· Production ML pipelines with monitoring and retraining
· Full-stack web apps with clean, polished UI/UX
· Custom apps for small businesses
· AI strategy consulting and roadmapping
🔧 Tech Stack:
AI/ML: GPT-4, Claude, LangChain, LangGraph, OpenClaw, PyTorch, TensorFlow, Hugging Face
Languages: Python, JavaScript, TypeScript
Frameworks: Next.js, FastAPI, Express, React
Cloud: AWS (Amplify, RDS, SES, Route 53, Lambda, EC2)
Databases: PostgreSQL, Vector DBs (Pinecone, ChromaDB, FAISS)
Voice/Phone: Twilio, VAPI, ElevenLabs
🤝 Working With Me:
I believe in clear communication and translating complex AI concepts into actionable business strategies. Whether you need a production-ready AI system, strategic guidance, or technical leadership, I deliver solutions that exceed expectations. Available for both short-term projects and long-term engagements.
3 languages: English, Arabic, French. I work across time zones.
Ready to talk? Send me a message with your project details.
Keywords: AI chatbot, AI agent, RAG, retrieval augmented generation, LLM, LangChain, LangGraph, vector database, conversational AI, AI receptionist, voice AI, AI automation, multi-agent, Python, Next.js, TypeScript, FastAPI, AWS, TensorFlow, PyTorch, OpenAI, Claude API, knowledge base, NLP, full stack, fine-tuning, MLOps, AI integration, AI voice agent, document processing, data extraction, OpenClaw, VAPI, Twilio
Steps for completing your project
After purchasing the project, send requirements so Mehdi can start the project.
Delivery time starts when Mehdi receives requirements from you.
Mehdi works on your project following the steps below.
Revisions may occur after the delivery date.
2
I analyze your documents and design the ingestion pipeline
2
I build the vector database and retrieval system