Ali RazaAI RAG & Full-Stack Specialist
I build production-ready AI applications with GPT-4, LangChain and scalable full-stack architectures — turning unstructured data into intelligent, real-time products.
Specialty
RAG · LLM Apps
About
I design and ship AI systems that combine large language models, retrieval pipelines and full-stack engineering to automate real business workflows.
From CRM automation to grounded document Q&A, my projects focus on accuracy, scalability, and clean architecture — not demos.
Selected Work
Production AI Projects
Three full-stack AI systems built end-to-end with modern LLM tooling.
AI CRM Automation System
Smart Data Extraction with FastAPI + MongoDB + OpenAI
Full-stack AI-powered CRM that converts unstructured customer messages into structured, actionable records. The LLM extracts name, email, location, issue and status, then auto-stores everything in MongoDB and surfaces it on a real-time dashboard.
Key Features
- AI-powered extraction of structured data from unstructured text
- Automatic storage of client records in MongoDB
- CRM-style dashboard with real-time updates
- Clean, scalable API architecture using FastAPI
Use Cases
CRM automation · Support workflow automation · Lead extraction · Email & message parsing
AI Website Chat Agent
Full-Stack AI System with Contextual Q&A
Analyze any website and chat with it. The system scrapes a user-provided URL, generates structured insights with an LLM, stores them as context, and powers a real-time multi-turn chat — RAG-style behavior end-to-end.
Key Features
- Website analysis from any user-provided URL
- AI-generated contextual understanding
- Multi-turn conversational chat interface
- Context-aware responses (RAG-style behavior)
Use Cases
Competitor analysis · Customer support · Business research · Lead qualification
RAG Pipeline
PDF Upload
80+ pages
Embeddings
OpenAI · Chroma
Retrieve
Top-k chunks
Grounded Answer
with page refs
AI Document Chatbot (RAG)
Chat with PDFs using GPT, LangChain & ChromaDB
Production RAG application for chatting with large PDFs (80+ pages). Uses Retrieval-Augmented Generation with OpenAI embeddings and ChromaDB so every answer is grounded in document content with page-level source references.
Key Features
- Upload and process large PDFs (80+ pages)
- Vector search using OpenAI embeddings
- Source-backed answers with page references
- Scalable FastAPI architecture
Use Cases
Legal document analysis · Financial reports (10-K) · Internal knowledge assistants · Contract review
Tech Stack
Tools I build with
Let's build something intelligent.
Have an AI, RAG or full-stack project in mind? I'd love to hear about it.
aliraza1230123@gmail.com
+92 347 8676893