You will get an AI Customer Support Agent with Ticket Handling and Automated Responses
Top Rated
Top Rated
Project details
Your support team is spending thousands of hours answering the same questions repeatedly. Every billing query, refund request, password reset, and FAQ that a human agent handles manually is a task an AI agent can resolve in under 3 seconds, 24 hours a day, at a fraction of the cost.
I build enterprise-grade AI customer support agents that auto-resolve 60 to 70% of incoming tickets, escalate complex cases to human agents with full context, and integrate directly into your existing helpdesk or CRM. Built with LangChain, GPT-4, FastAPI, and PostgreSQL, every system is deployed to production and monitored in real time.
What you get: a fully autonomous support agent trained on your knowledge base, with ticket classification, auto-resolution, human escalation logic, response time under 3 seconds, and a metrics dashboard showing resolution rates, cost savings, and agent hours saved.
Real results from production deployments: $3,733 monthly savings per 10,000 tickets, 42 agent hours saved per week, 2.7 second average response time, and 24/7 availability with zero downtime.
I build enterprise-grade AI customer support agents that auto-resolve 60 to 70% of incoming tickets, escalate complex cases to human agents with full context, and integrate directly into your existing helpdesk or CRM. Built with LangChain, GPT-4, FastAPI, and PostgreSQL, every system is deployed to production and monitored in real time.
What you get: a fully autonomous support agent trained on your knowledge base, with ticket classification, auto-resolution, human escalation logic, response time under 3 seconds, and a metrics dashboard showing resolution rates, cost savings, and agent hours saved.
Real results from production deployments: $3,733 monthly savings per 10,000 tickets, 42 agent hours saved per week, 2.7 second average response time, and 24/7 availability with zero downtime.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAIAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$699
|
Standard
$1,500
|
Advanced
$2,800
|
|---|---|---|---|
| Delivery Time | 5 days | 12 days | 20 days |
Number of Revisions | 1 | 2 | 3 |
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 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100Frequently asked questions
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About Muhammad
AI Agent Developer and Architect | Multi-Agent Systems | LangGraph n8n
100%
Job Success
Sialkot, Pakistan - 5:01 pm local time
🎙️ AI Voice Agents (Vapi, ElevenLabs, Twilio)
⚙️ Workflow Automation (n8n, Make, GoHighLevel)
🧠 LLM Integration (GPT-4, Claude, Gemini, LLaMA)
🔗 API Development & System Integration
👨💻 5+ Years Building Production AI Solutions
Businesses come to me when they need autonomous AI systems that actually run in production, not demos or prototypes.
I build multi-agent pipelines, AI voice agents, and intelligent workflow automations using Python, LangGraph, CrewAI, FastAPI, and the full LLM stack (GPT-4o, Claude, Gemini). Everything I deliver is Docker-containerized, deployed on Railway or AWS, monitored, and documented.
Recent results from live production systems:
- 77% cost reduction on customer support operations ($12K to $2.8K/month)
- 95% faster invoice processing with 98%+ GPT-4o extraction accuracy
- 85% faster appointment booking with 100% after-hours coverage
- 83% time savings on bookkeeping automation ($1,200 to $130/month)
WHAT I BUILD
Multi-Agent Systems
LangGraph and CrewAI orchestration, tool-calling agents, memory management, self-evaluation loops, MCP and A2A protocol integration, Pydantic AI, AutoGen. Built for enterprise scale with full audit trails and human-in-the-loop escalation.
AI Voice Agents
Inbound and outbound AI calling via Vapi and Twilio. ElevenLabs voice synthesis. Real-time STT/TTS. Lead qualification, appointment booking, 24/7 receptionist automation. GoHighLevel and HubSpot CRM sync.
Workflow Automation with AI Decision Layers
n8n agent workflows, Make scenarios, GoHighLevel pipeline automation. The difference from standard automation: every workflow has an LLM decision layer, not just if/then logic.
RAG and Document Intelligence
Semantic chunking, hybrid search, reranking with Pinecone and pgvector. GPT-4o vision for invoice and document extraction. Three-way matching, ERP sync (QuickBooks, Xero, SAP, NetSuite). AP automation with 98%+ accuracy.
Production Infrastructure
FastAPI backends, PostgreSQL and Supabase, Redis, Docker, Railway, AWS. Every system ships with monitoring, error handling, and documentation. Not a side project setup.
USE CASES
AI receptionists and intake agents | Appointment booking and rescheduling | Lead qualification and CRM automation | Customer support automation | Accounts payable and invoice processing | Facility management ops | Recruitment screening | Sales pipeline automation | B2B lead generation | WhatsApp and SMS automation
TECH STACK
Agents: LangGraph, CrewAI, AutoGen, Pydantic AI, LangChain
LLMs: GPT-4o, Claude 3.5/3.7, Gemini 2.5, Llama 3, Mistral
Voice: Vapi, ElevenLabs, Twilio, Bland.ai
Automation: n8n, Make, GoHighLevel
Backend: Python, FastAPI, REST APIs
Data: PostgreSQL, Supabase, MongoDB, Redis, Pinecone, Chroma
Infra: Docker, Railway, AWS, GitHub CI/CD
Integrations: QuickBooks, Xero, SAP, Stripe, PandaDoc, Google Calendar, HubSpot
If you are building an AI system that needs to run reliably in production, let's talk.
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.
Knowledge Base and Setup
Collect your FAQs, support docs, and ticket history. Set up the vector knowledge base, configure GPT-4 classification, and define escalation rules.
Agent Build and Integration
Build the ticket processing pipeline, auto-resolution logic, human escalation flow, and connect to your helpdesk, CRM, or chat platform. Run accuracy tests.