You will get a production-ready AI agent with LangChain/LangGraph, AWS Bedrock
Project details
Most businesses requesting "AI automation" end up with a single-prompt chatbot that breaks the moment the conversation deviates from the script. This catalog delivers something different: a stateful, multi-step AI agent that receives a trigger, classifies intent, routes decisions, calls external APIs, handles exceptions, and confirms the final state, without a human in the loop at every turn.
What you receive:
• Stateful LangGraph agentic workflow with conditional routing, branching logic, and fallback handling for every edge case
• Intent classification layer that distinguishes between user actions, queries, and ambiguous inputs — and responds appropriately to each
• Session memory via Amazon DynamoDB — users never repeat context within a conversation, agents never lose state between turns
• API integrations to your existing systems — booking backends, CRMs, ERPs, databases, or third-party services
• Deployed on AWS Lambda and API Gateway — serverless, scalable, production-ready from day one
• Full project handover: architecture diagram, clean source code, deployment guide, and documentation
What you receive:
• Stateful LangGraph agentic workflow with conditional routing, branching logic, and fallback handling for every edge case
• Intent classification layer that distinguishes between user actions, queries, and ambiguous inputs — and responds appropriately to each
• Session memory via Amazon DynamoDB — users never repeat context within a conversation, agents never lose state between turns
• API integrations to your existing systems — booking backends, CRMs, ERPs, databases, or third-party services
• Deployed on AWS Lambda and API Gateway — serverless, scalable, production-ready from day one
• Full project handover: architecture diagram, clean source code, deployment guide, and documentation
Programming Languages
PythonCoding Expertise
Cross Browser & Device Compatibility, Security, DesignWhat's included
| Service Tiers |
Starter
$800
|
Standard
$1,800
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 5 |
Design Customization | |||
Content Upload | |||
Responsive Design | |||
Source Code |
Frequently asked questions
About Muhammad
AI Engineer | AI Agents | RAG Systems | AWS Bedrock | LangGraph
Islamabad, Pakistan - 10:39 am local time
If your team is still pulling reports, waiting on analysts, or logging into multiple systems to answer one business question, that is exactly the problem these agents eliminate.
What I build:
→ Enterprise RAG systems with role-based access and ERP integration
→ Multi-agent LangGraph workflows for retrieval, classification, and action
→ Text-to-SQL agents for non-technical teams querying live databases in plain English
→ Conversational interfaces that replace manual intake, booking, and reporting workflows
▸ Holding Group AI Assistant, 9 subsidiaries, 500+ employees
A conglomerate's HR, operations, manufacturing, and executive teams all queried different systems manually. Built Nexus: one AI interface where each of the 4 user tiers sees only what they are allowed to see, enforced at the data retrieval layer, not the UI. Leave booking went from a 3-screen SAP process to a single chat message. 12+ manual workflows replaced across 10 companies. Leave requests that took 3 SAP screens now take one message.
▸ Conversational Product Search, E-commerce Platform
Customers searching in natural language were hitting a keyword-match wall and dropping off before finding products. Built a search agent on AWS Kendra that asks smart follow-up questions, narrows intent across multiple turns, then returns a shortlist with a plain-English reason for each match. Search became a personal shopper. Discovery drop-off fell. Customers who described what they needed got to the right product in under 3 exchanges.
▸ Self-Correcting Text-to-SQL Agent, Operations Team
90% of employees had no access to live data in Amazon Redshift because they could not write SQL. Built an agent that generates SQL, runs it, then checks whether the result actually answered the question, and rewrites if not. Wrong answers never reach the user. Non-technical staff now self-serve ~90% of operational data queries. Questions answered in seconds that previously required 12-24 hour analyst turnaround.
▸ Medical Transport Booking Assistant, NEMT Provider
Elderly and disabled patients were booking rides through phone queues with hold times and frequent scheduling errors. Built a conversational booking agent that confirms every action in plain English before executing it. Booking time dropped from an average phone call to under 2 minutes. Manual intake load for staff fell significantly. Scheduling errors from miscommunication decreased.
Core stack: LangGraph | AWS Bedrock | AWS Knowledge Bases | Amazon Kendra | Amazon Redshift | LangChain | LlamaIndex | OpenAI API | Gemini API | FastAPI | Pinecone | Qdrant | pgvector | Hybrid Search | Python | Docker | AWS (EC2, Lambda, S3, DynamoDB)
Industries: Enterprise | Healthcare | E-commerce | Operations | FinTech
If your team spends more than 5 hours a week answering questions that live in your documents, databases, or ERP, message me with the workflow. I will map exactly where an AI agent replaces it, at no cost, in one message.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and architecture (Days 1-2)
We align on the full workflow: triggers, intent paths, API integrations, edge cases, and confirmation logic. I deliver an architecture diagram before writing a line of code so you can validate the design.
Build, test, and deploy
I build the LangGraph workflow, integrate your APIs, implement session memory, test against real edge cases, and deploy to AWS Lambda and API Gateway. You receive source code, deployment guide, and a walkthrough of the live system.