You will get Production AI Agent System LangChain, Claude, OpenAI & LangGraph
Rising Talent
Rising Talent
Project details
Build an autonomous AI workforce for your business — one that works around the clock without manual oversight.
I design and develop custom AI agent systems and multi-agent workflows using LangGraph and Claude, built to automate the complex, multi-step processes that used to require constant human supervision. Deep research automation, data extraction pipelines, self-correcting agent workflows, RAG-powered knowledge systems — whatever the process, you get a production-ready AI automation system built around your exact business logic, not a generic template.
Here's what makes this different from a basic chatbot: these are true multi-agent systems with advanced reasoning and task planning. Each agent doesn't just respond — it plans, executes, verifies its own work, and delivers a finished result, running continuously without supervision. Combined with retrieval-grounded RAG for accuracy and built-in cost optimization so scaling doesn't inflate your API spend, this is a complete AI business process automation system, not a proof of concept.
The outcome: your manual workflows become autonomous, reliable systems — freeing your team to focus on strategy while the agents handle execution.
I design and develop custom AI agent systems and multi-agent workflows using LangGraph and Claude, built to automate the complex, multi-step processes that used to require constant human supervision. Deep research automation, data extraction pipelines, self-correcting agent workflows, RAG-powered knowledge systems — whatever the process, you get a production-ready AI automation system built around your exact business logic, not a generic template.
Here's what makes this different from a basic chatbot: these are true multi-agent systems with advanced reasoning and task planning. Each agent doesn't just respond — it plans, executes, verifies its own work, and delivers a finished result, running continuously without supervision. Combined with retrieval-grounded RAG for accuracy and built-in cost optimization so scaling doesn't inflate your API spend, this is a complete AI business process automation system, not a proof of concept.
The outcome: your manual workflows become autonomous, reliable systems — freeing your team to focus on strategy while the agents handle execution.
AI Algorithms
Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Transformer Model, YOLOAI Applications
AI Text-to-Speech, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, GPT-3, GPT-4, GPT-J, Jurassic-2, LLaMA, Naive Bayes Classifier, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$89
|
Standard
$200
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 21 days |
Number of Revisions | 2 | 4 | 6 |
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 Shahzad
AI Engineer | RAG Chatbots & AI Agents | LangChain, Python, FastAPI
Hyderabad, Pakistan - 8:08 am 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 Discovery
You walk me through your specific workflow and business goals. Together we map out the exact agent roles and decision points your system needs.
System Architecture
I design the workflow orchestration logic and choose the right model for each task — Claude, GPT-4, or a mix, depending on what each step requires.