You will get an ai agents customised to your business
Project details
I’ll build a Retrieval-Augmented Generation (RAG) chatbot that answers with facts from your own documents — not random internet data.
Using LangChain, Hugging Face, and Pinecone, I’ll create an intelligent assistant that retrieves relevant info from your PDFs, manuals, or FAQs, and generates accurate, cited, and human-like answers in real time.
You’ll get:
🧩 End-to-end RAG pipeline (chunking → embedding → retrieval → generation)
⚙️ LangChain orchestration with prompt templates and safety guards
🪴 Pinecone vector DB for lightning-fast semantic search
🤖 LLM integration (Hugging Face / OpenAI / Gemini)
🌐 FastAPI backend + optional React/Next.js UI
A production-ready, explainable AI assistant that responds with your verified knowledge — not guesses.
Using LangChain, Hugging Face, and Pinecone, I’ll create an intelligent assistant that retrieves relevant info from your PDFs, manuals, or FAQs, and generates accurate, cited, and human-like answers in real time.
You’ll get:
🧩 End-to-end RAG pipeline (chunking → embedding → retrieval → generation)
⚙️ LangChain orchestration with prompt templates and safety guards
🪴 Pinecone vector DB for lightning-fast semantic search
🤖 LLM integration (Hugging Face / OpenAI / Gemini)
🌐 FastAPI backend + optional React/Next.js UI
A production-ready, explainable AI assistant that responds with your verified knowledge — not guesses.
AI Algorithms
AdaBoost, Large Language Model, Long Short-Term Memory Network, Transformer ModelAI Applications
AI Chatbot, AI Mobile App Development, AI Text-to-Image, Conversational AI, Natural Language Understanding, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, PyTorch, Replit, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, DALL-E, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$50
|
Standard
$350
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 7 days | 15 days | 29 days |
Number of Revisions | 1 | 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.
Additional Revision
+$10Frequently asked questions
About Mahafuzul Islam
Machine Learning Deep Learning Engineer AI Solutions Full Stack
Dhaka, Bangladesh - 8:55 pm local time
Hello! 👋 I’m Shawon, a passionate Machine Learning Engineer with strong expertise in Deep Learning, AI-powered applications, and Full Stack Web Development.
I specialize in building intelligent systems that combine cutting-edge machine learning models with robust backend and frontend solutions. With experience in React, Node.js, Spring Boot, FastAPI, and MongoDB, I bridge the gap between AI research and production-ready applications.
Steps for completing your project
After purchasing the project, send requirements so Mahafuzul Islam can start the project.
Delivery time starts when Mahafuzul Islam receives requirements from you.
Mahafuzul Islam works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1 — Data Understanding and Preprocessing
I’ll analyze your provided documents, decide on the optimal chunking and embedding strategy, and preprocess the text for clean retrieval.
Step 2 — Vector Index Creation
I’ll embed the chunks using your chosen model (OpenAI or Hugging Face) and store them in a Pinecone (or FAISS) vector database for efficient semantic search.