You will get an AI Agent System | LangGraph + RAG + Backend + Cloud Deployment
Project details
Most AI agent projects never leave the developer's laptop. The architecture looks clean in a notebook, breaks the moment it touches real data, and the client is left with a demo they cannot ship.
I build systems that run in production from day one. Every delivery includes the agent logic, backend infrastructure, database layer, and cloud deployment. Deployed, tested, and documented so your team can own it without me.
What you get with this package:
• Production grade AI agent or multi-agent system built with LangGraph. Tool calling, memory persistence, retry logic, and structured outputs handled properly
• RAG pipeline connected to your actual documents, database, or knowledge base. Accurate, cited responses with no hallucinations
• FastAPI backend with async processing, authentication, and clean API structure your team can extend
• Advanced tier includes a complete deployable AI-powered web application
• Cloud deployment on AWS, Azure, or GCP with Docker and CI/CD. Environment configs, logging, and monitoring included
• Voice support on Advanced tier. Whisper, ElevenLabs, and LiveKit for real-time pipelines
• Full documentation and two weeks post launch support on every tier
I build systems that run in production from day one. Every delivery includes the agent logic, backend infrastructure, database layer, and cloud deployment. Deployed, tested, and documented so your team can own it without me.
What you get with this package:
• Production grade AI agent or multi-agent system built with LangGraph. Tool calling, memory persistence, retry logic, and structured outputs handled properly
• RAG pipeline connected to your actual documents, database, or knowledge base. Accurate, cited responses with no hallucinations
• FastAPI backend with async processing, authentication, and clean API structure your team can extend
• Advanced tier includes a complete deployable AI-powered web application
• Cloud deployment on AWS, Azure, or GCP with Docker and CI/CD. Environment configs, logging, and monitoring included
• Voice support on Advanced tier. Whisper, ElevenLabs, and LiveKit for real-time pipelines
• Full documentation and two weeks post launch support on every tier
AI Algorithms
Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Text-to-Speech, AI-Generated Video, Automatic Speech Recognition, Conversational AI, Image Recognition, Text RecognitionAI Development Language
PythonAI Tools
Gradio, Hugging Face, Microsoft 365 Copilot, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, Midjourney AI, Stable Diffusion, WhisperWhat'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 | 4 |
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.
Fast Delivery
+$200 - $500
Additional Revision
+$50Frequently asked questions
About Faisal
AI Agent Engineer | LangGraph, RAG, Voice AI, Multi-Agent Systems
Lahore, Pakistan - 1:25 pm local time
Most AI engineers on Upwork stop at the model. I go further.
WHAT I BUILD
▸ AI Agents & Multi-Agent Systems
LangGraph agents with deterministic state machines, tool-calling loops, memory persistence, and exponential backoff retry logic. Multi-agent orchestration using CrewAI, AutoGen, and OpenAI Agents SDK with Claude MCP for context aware tool use. Built for SaaS products, internal operations, and customer facing automation . not toy demos, production pipelines with observability and logging.
▸ RAG Systems & Knowledge Assistants
Your documents, your database, your internal knowledge . made conversational and accurate. RAG pipelines built with Pinecone, pgvector, or ChromaDB using OpenAI or Claude with token-efficient retrieval strategies, hybrid search, and re-ranking for grounded, cited answers over thousands of files. No hallucinations.
▸ Voice AI & Conversational Agents
Real-time voice pipelines using ElevenLabs, Whisper, LiveKit, and Twilio. Appointment booking agents, inbound call handlers, CRM-integrated voice systems with sub-second latency . built for businesses that need AI handling actual customer interactions 24/7.
▸ Workflow Automation
n8n and Make workflows connecting your CRM, Slack, Gmail, WhatsApp, and APIs into one intelligent pipeline. Multi-channel lead intake, automated follow-ups, data sync across systems . operational overhead drops, your team focuses on work that matters.
▸ Full-Stack AI Products
FastAPI backends, Next.js frontends, PostgreSQL and MongoDB . deployed on AWS EC2 or ECS with Docker, Nginx, and CI/CD. If you need an AI feature shipped into a working product, not a notebook, that's exactly what I do.
MY TECH STACK
Agentic AI: LangGraph · LangChain · CrewAI · AutoGen · OpenAI Agents SDK · Claude MCP · LlamaIndex
LLMs: OpenAI · Claude · Gemini · Groq · DeepSeek
RAG: Pinecone · pgvector · ChromaDB · FAISS · Weaviate
Voice: ElevenLabs · Whisper · LiveKit · Twilio · Retell
Automation: n8n · Make · Zapier · Webhooks
Backend: Python · FastAPI · Node.js · Express
Frontend: Next.js · React · Tailwind CSS
Cloud & DevOps: AWS EC2 · ECS · EKS · Docker · Kubernetes · CI/CD · Nginx · Cloudflare
HOW WE WORK TOGETHER
Discovery . 30 minute call to understand your workflow, pain points, and current stack. I define the right architecture before writing a single line of code.
Scoped Proposal . Clear breakdown of deliverables, timeline, and cost. No surprises.
Build & Iterate . Weekly check-ins, live demos, clean commits. You always know the status.
Deploy & Hand Off . Production deployment with documentation and two weeks of post-launch support. I don't disappear after delivery.
If you have an AI project that needs to be built properly . not prototyped, not demoed, actually shipped . send me a message. I respond within a few hours and will tell you honestly whether I can help and exactly how.
Steps for completing your project
After purchasing the project, send requirements so Faisal can start the project.
Delivery time starts when Faisal receives requirements from you.
Faisal works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery and System Design
I go through your requirements in detail, ask the right questions, and define the full architecture agent structure, tool integrations, data flow, and deployment plan so there are no surprises mid-build.
Agent Pipeline and RAG Build
I build the LangGraph agent with tool calling, memory, and structured reasoning. Where RAG is needed, I set up the vector store, embedding pipeline, and retrieval layer connected to your actual data.