You will get an autonomous AI customer support agent with MCP and RAG
Project details
I built an autonomous AI agent that handles customer support end-to-end: order status, refunds, tracking, FAQs — all without human intervention. Uses MCP protocol + RAG for accuracy. 70 tests, 80% coverage. Production-ready with Docker, monitoring, and API.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging FaceAI Models
BERT, LLaMAWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 28 days |
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 |
About Mohammed
AI/ML Engineer
Nanterre, France - 1:02 am local time
𝗪𝗵𝗮𝘁 𝗜 𝗗𝗼:
* LLM & RAG Systems
- Document Q&A pipelines with minimal hallucination
- Text-to-SQL for business analytics
- Email automation using Generative AI
- LLM fine-tuning (LoRA, QLoRA)
* Computer Vision & Document AI
- Document Layout Analysis & OCR pipelines
- Information extraction from research papers
- Custom model training for domain-specific documents
* MLOps & Production
- Clean, tested code (90%+ coverage)
- FastAPI endpoints & Docker deployment
- End-to-end pipeline design
𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸:
Python, PyTorch, HuggingFace, LangChain, Transformers, FastAPI, Docker, PostgreSQL, Databricks, PySpark
𝗠𝗼𝗱𝗲𝗹𝘀:
GPT, Claude, Mistral, Llama, PaddleOCR, EasyOCR, YOLO
𝗪𝗵𝘆 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵 𝗠𝗲:
- Production-ready code with full documentation
- Clear communication and fast delivery
- Experience with real-world business problems
Let's discuss your AI project.
Steps for completing your project
After purchasing the project, send requirements so Mohammed can start the project.
Delivery time starts when Mohammed receives requirements from you.
Mohammed works on your project following the steps below.
Revisions may occur after the delivery date.
Intent Analysis
I analyze your customer queries and define the intents the agent needs to handle
MCP Servers Setup
I build the MCP servers for database access, RAG knowledge base, and action execution