You will get a Production level AI MVP within 2 days | LangChain, RAG, Claude, chatbot
Rising Talent
Rising Talent
Project details
Stop waiting months for an MVP. I am a Lead AI-Native Developer specializing in rapid, production-grade AI applications.
The market is flooded with beginners who simply copy-paste generated scripts that break in production. I bring actual computer science engineering rigor to AI-native development. My 2025 Cursor AI usage report verifies my extreme efficiency: I have processed over 3.15 Billion tokens and orchestrated 7,800+ autonomous agents.
By leveraging elite models like Claude alongside my native Ubuntu development environment, I architect systems at unmatched speeds without sacrificing scalability, maintainability, or security. Whether you need a sophisticated LangChain workflow, a custom database integration, or a full-stack MVP, I build robust systems designed to handle real users.
Please review my portfolio below to see my verified enterprise-level AI applications and RAG systems. Let’s turn your idea into a deployed, revenue-ready MVP in days.
The market is flooded with beginners who simply copy-paste generated scripts that break in production. I bring actual computer science engineering rigor to AI-native development. My 2025 Cursor AI usage report verifies my extreme efficiency: I have processed over 3.15 Billion tokens and orchestrated 7,800+ autonomous agents.
By leveraging elite models like Claude alongside my native Ubuntu development environment, I architect systems at unmatched speeds without sacrificing scalability, maintainability, or security. Whether you need a sophisticated LangChain workflow, a custom database integration, or a full-stack MVP, I build robust systems designed to handle real users.
Please review my portfolio below to see my verified enterprise-level AI applications and RAG systems. Let’s turn your idea into a deployed, revenue-ready MVP in days.
AI Algorithms
AdaBoost, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Regression AnalysisAI Applications
AI Chatbot, AI Mobile App Development, AI Text-to-Image, AIOps, Conversational AI, Natural Language Generation, Sentiment Analysis, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$69
|
Standard
$249
|
Advanced
$649
|
|---|---|---|---|
| Delivery Time | 2 days | 2 days | 10 days |
Number of Revisions | 1 | 2 | 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 |
Frequently asked questions
About Shahzad
AI Engineer | RAG Chatbots & AI Agents | LangChain, Python, FastAPI
Hyderabad, Pakistan - 7:55 pm local time
My work covers the full pipeline: retrieval architecture, agent design, voice integration, and cost optimization, so the LLM bill doesn't become a second project six months in. I don't just wire up an API to a model, I build systems that keep answering correctly and keep costing what they should, long after launch.
My expertise includes:
RAG Chatbots & Retrieval Architecture (Hybrid Search, Re-ranking, Semantic Caching)
Autonomous AI Systems & Multi-Step Agent Workflows (LangGraph, Tool Calling, Memory)
Voice AI Agents & Conversational IVR (Inbound Calls, Appointment Booking, Automated Follow-ups)
LLM Cost Optimization (Query Compression, Token Budgeting, Sentence-Aware Chunking)
Vector Databases & Semantic Search (Pinecone, pgvector, ChromaDB, FAISS)
AI Workflow Automation & Custom Chatbots / Internal Knowledge Bases
Full-Stack AI Applications (Python, FastAPI, n8n, Zapier)
Cloud Deployment & Integration (AWS, Google Sheets/Calendar API, Docker)
Recent projects include:
Legal Document Intelligence Platform — Designed a RAG engine for plain-language Q&A over complex legal documents, plus a generation engine that drafts custody agreements, contracts, and divorce filings from structured input. Cut manual drafting time from hours to minutes.
Shopify Live RAG Chatbot Pipeline — Built a crawler that continuously feeds live product data from a client's Shopify store into a RAG chatbot, giving customers accurate, real-time answers with zero manual updates.
Voice AI Agent for a Salon (Vapi) — Built an inbound voice assistant that answers calls, books appointments directly into Google Sheets, and sends automated confirmation emails — full hands-off front desk automation.
LeanRAG — Cost-Optimized RAG Architecture — Implemented four layered cost-optimization techniques (semantic caching, query compression, token budget enforcement, sentence-aware chunking) that cut LLM inference costs by 30–40% without losing answer quality.
Karachi Air Quality Index Forecasting System — Built an end-to-end ML pipeline (data collection, preprocessing, model training, deployment) as a usable predictive tool for a non-technical team.
What you can expect:
Clean, maintainable, production-quality code with clear documentation
Retrieval and voice systems tuned for accuracy, not just plausible-sounding answers
Cost-aware architecture — token budgets and optimization built in from day one
Clear communication and reliable post-deployment support
I work with startups, growing businesses, and teams building AI products for measurable results, not experimental prototypes. If you're dealing with a support bottleneck, need a RAG chatbot that actually knows your documents, an autonomous system that runs a workflow end-to-end, or an LLM bill that's gotten out of hand, message me and I'll recommend the most practical approach.
Steps for completing your project
After purchasing the project, send requirements so Shahzad can start the project.
Delivery time starts when Shahzad receives requirements from you.
Shahzad works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Gathering
I'll review your project details, existing code/assets, and data sources, then confirm scope, architecture, and tech stack before starting.
Proposal & Project Plan
I'll send a detailed scope of work with milestones, timeline, and pricing. Once approved, we'll set up communication and tracking.