You will get Customized AI Chatbot or RAG-Powered Web Application
Project details
I will build a fully functional AI-powered web application tailored to your use case, whether it's an intelligent chatbot, a document Q&A system, a RAG pipeline over your own data, or an LLM-integrated workflow. The system will include a clean React frontend, a Python (FastAPI/Django) backend, and a properly engineered AI layer with source citations, context memory, and hallucination controls. I've built legal AI assistants, Islamic knowledge platforms, and multi-agent systems that run reliably in production, not just demos.
Programming Languages
PHP, JavaScript, PythonCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, DesignWhat's included $20
These options are included with the project scope.
$20
- Delivery Time 7 days
- Number of Revisions 3
- Number of Pages 6
- Design Customization
- Content Upload
- Responsive Design
- Source Code
About Mujeeb
Full-Stack Developer | React, Python | AI/LLM & RAG Engineer
Peshawar, Pakistan - 12:18 am local time
Delivered 300+ UI components with reusable patterns that cut rework by ~80%. Built RAG systems hitting 93% answer accuracy. Reduced LLM API costs by 40% through intelligent routing. Automated workflows that eliminated 28+ hours/week of manual monitoring. Every project in my portfolio is real, shipped, and backed by numbers.
▸ FULL-STACK WEB & MOBILE DEVELOPMENT:
React.js, Next.js, Flutter, Django, Node.js, Tailwind CSS, TypeScript, built pixel-perfect from Figma designs across web and cross-platform mobile. Delivered production apps spanning Islamic education platforms, charity websites, fintech POS systems, legal AI assistants, and SaaS dashboards. 300+ screens and components shipped with zero design drift.
✔ Figma to React/Flutter, pixel-perfect, every time
✔ REST API design, authentication (JWT, Google OAuth), role-based access
✔ Performance-optimized: ~25% load time improvement through API payload shaping and query tuning
▸ AI, LLM & RAG ENGINEERING:
LangGraph multi-agent pipelines, Retrieval-Augmented Generation (RAG), vector search (Qdrant), HyDE query transformation, cross-encoder reranking, OpenAI API, LangChain, LLM fine-tuning (Mistral-7B via QLoRA), and intelligent routing systems.
✔ 93% answer accuracy on Afghan Civil Law RAG system across 9,000+ legal provisions
✔ 3-stage retrieval pipeline (HyDE → Hybrid BM25/Vector → Neural reranking) cut retrieval noise by ~40%
✔ LLM routing system at 92% accuracy, saving ~40% in external API costs
✔ Mistral-7B fine-tuned with QLoRA (4-bit) on 40K samples, 60% training cost reduction
✔ ~2.3s average response time on production RAG pipeline serving 400K+ records
▸ PYTHON AUTOMATION & WEB SCRAPING
Selenium, Playwright, BeautifulSoup, FastAPI, Pandas, automated data pipelines, market intelligence systems, and document processing workflows that replace manual work with reliable, always-on code.
✔ Eliminated 28+ hours/week of manual e-commerce monitoring across 1,000+ SKUs
✔ Built real-time competitor pricing extraction with instant email alerts on market position changes, reaction time under 5 minutes
✔ Inventory processing reduced from 4 hours to under 5 minutes
✔ Product listing time cut from 4+ minutes to under 40 seconds
✔ 95%+ pipeline uptime under dynamic anti-bot conditions with auto-recovery built in
✔ Automated structured Excel report generation after every scan cycle for audit-ready business records
▸ BACKEND & API DEVELOPMENT:
Django, FastAPI, Node.js/Express.js, PHP/Laravel, PostgreSQL, MySQL, SQLite, AWS (Lambda, S3, DynamoDB), Docker, Supabase. Built FBR-compliant POS and invoicing systems, serverless AI backends deployed on AWS Lambda, financial document ingestion pipelines, and multi-tenant SaaS backends.
▸ TECH STACK:
Frontend: React.js | Next.js | Flutter | TypeScript | JavaScript | Tailwind CSS | HTML5/CSS3
Backend: Python | Django | FastAPI | Node.js | Express.js | PHP | Laravel
AI/ML: LangGraph | LangChain | OpenAI API | Qdrant | RAG | HyDE | Cross-Encoder Reranking | LLM Fine-tuning
Automation: Selenium | Playwright | BeautifulSoup | Pandas | pdfplumber | Puppeteer
Cloud & DevOps: AWS Lambda | S3 | DynamoDB | Docker | Supabase | Git
Databases: PostgreSQL | MySQL | SQLite | MongoDB | Redis
▸ WHY CLIENTS WORK WITH ME
Most developers hand you code. I hand you outcomes.
Every project I take has measurable goals attached to it, faster load times, eliminated manual hours, accurate AI responses, clean UI from your Figma file. If there is no clear win at the end, I flag it before we start, not after.
✔ Pixel-perfect execution: 300+ screens delivered from Figma with zero design drift. What the designer drew is what the user sees.
✔ Metrics-driven mindset: From 93% RAG accuracy to 40% API cost reduction, I track what actually matters to your business.
✔ Full ownership: I don't pass off problems between "frontend" and "backend." I own the entire stack until it ships and works.
✔ Flexible availability: Based in Pakistan (PKT, UTC+5), available to overlap with EU, Gulf, and partial US East Coast hours. Quick turnarounds, responsive communication.
✔ Clean, maintainable code: Built to be handed over, extended, or scaled. No spaghetti, no magic, no "only I can maintain this."
✔ AI-ready: Not just integrating APIs, but building intelligent systems: RAG pipelines, multi-agent workflows, fine-tuned models, and automation that actually runs in production.
✔ Fast delivery with modern tooling: I also work fluently with AI-assisted platforms like Lovable, Bolt.new, v0, and Cursor, and know when to use them vs when to build from scratch. The result is the same quality, often in half the time.
Steps for completing your project
After purchasing the project, send requirements so Mujeeb can start the project.
Delivery time starts when Mujeeb receives requirements from you.
Mujeeb works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Data Review
I review your use case, documents, and data sources thoroughly, then confirm the AI pipeline architecture and tech stack before any development begins.
RAG Pipeline & Backend Setup
I build the core AI engine, document ingestion, vector embeddings, retrieval logic, reranking, and the FastAPI backend with LLM integration and source citation.