You will get Advanced AI Agents & RAG Chatbots with LangGraph
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Top Rated
Project details
Standard chatbots answer questions. This service delivers something fundamentally different, agentic AI systems and RAG-powered chatbots built on LangGraph and LangChain that reason through problems, retrieve context from your actual data, and take real actions across your tools and systems.
A RAG chatbot connects directly to your documents, knowledge base, database, or Google Drive, embedding content into a vector store using FAISS, Chroma, Pinecone, or Qdrant and returning accurate, source-linked answers from your own data. A LangGraph AI agent goes further, maintaining stateful multi-step reasoning, calling APIs, and completing tasks autonomously without intervention.
What you can customise: the LLM (OpenAI GPT-4, Anthropic Claude, Gemini, or open-source via Ollama), data sources, vector database, agent tools, and backend integrations.
Typical use cases include internal knowledge assistants, customer support automation, document intelligence, multi-agent pipelines for complex business workflows, and AI systems that replace manual research, data retrieval, or decision routing. Every system is delivered production-ready, documented, and built to handle real users at real scale.
A RAG chatbot connects directly to your documents, knowledge base, database, or Google Drive, embedding content into a vector store using FAISS, Chroma, Pinecone, or Qdrant and returning accurate, source-linked answers from your own data. A LangGraph AI agent goes further, maintaining stateful multi-step reasoning, calling APIs, and completing tasks autonomously without intervention.
What you can customise: the LLM (OpenAI GPT-4, Anthropic Claude, Gemini, or open-source via Ollama), data sources, vector database, agent tools, and backend integrations.
Typical use cases include internal knowledge assistants, customer support automation, document intelligence, multi-agent pipelines for complex business workflows, and AI systems that replace manual research, data retrieval, or decision routing. Every system is delivered production-ready, documented, and built to handle real users at real scale.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Speech, Conversational AI, Image Processing, Natural Language Understanding, Sentiment Analysis, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Gradio, Hugging Face, PyTorch, Replit, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, Jurassic-2, LLaMA, Midjourney AI, Naive Bayes Classifier, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$110
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 15 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 |
Frequently asked questions
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ST
Sidali T.
May 25, 2026
Independent annotator for academic ML paper — 63 items, ~75 min ($30)
CW
Chris W.
Apr 25, 2026
No-Code Automation & AI Integration Assistant
Muhammad Khalid was amazing to work with. I would recommend him and hope to work with him again when the need emerges. Very detailed, highly skilled and very intelligent.
TS
Tabasum S.
Mar 11, 2026
Need Simple n8n Workflow Setup
Khalid did an excellent job setting up the n8n workflow for us. He quickly connected the webhook with Google Sheets and ensured the incoming data was formatted and stored correctly. Communication was smooth, delivery was fast, and the automation works perfectly. Highly recommended for n8n and automation tasks.
AH
Ali H.
Mar 2, 2026
Backend Bug Fixing & Optimization
Great experience working with Khalid! He quickly fixed backend issues, and improved code quality. Highly professional and reliable.
AO
Abdullah O.
Nov 16, 2025
Django → API Integration for Next.js Knowledge Base System (KBS)
Khalid is a highly skilled developer who pays attention to details and ensures everything is done perfectly. ✅
Khalid provided valuable suggestions that improved the project, communicated clearly throughout the process 💬, and delivered exceptional results 🚀.
If you’re looking for a reliable, talented, and professional developer, Khalid is the right choice. 🤝
I will definitely be hiring Khalid again for future projects! 🔥✨
Khalid provided valuable suggestions that improved the project, communicated clearly throughout the process 💬, and delivered exceptional results 🚀.
If you’re looking for a reliable, talented, and professional developer, Khalid is the right choice. 🤝
I will definitely be hiring Khalid again for future projects! 🔥✨
About Muhammad
AI Agent & Automation, MERN, LangChain, n8n, RAG, FastAPI, Django, AWS
100%
Job Success
Karachi, Pakistan - 5:41 am local time
A K-12 SaaS that saves teachers hours of manual grading weekly. A B2B logistics platform built on MERN that eliminated dispatch overhead and syncs Shopify, ERPs, and WMS in real time. A banking assistant that executes real transfers from a single sentence. A natural language interface that cut developer dependency for database reporting to zero. Those are my last four projects.
Python, FastAPI, Django, LangChain. architecture to production. Custom software built to survive real users, real data, and real scale.
Not demos. Not prototypes. Systems with clean documentation designed to keep running after han
𝐖𝐡𝐚𝐭 𝐈 𝐛𝐮𝐢𝐥𝐝:
→ SaaS Backend Development: Your backend shouldn't be the reason your product can't grow. Scalable API architecture on FastAPI, Django, and MERN, PostgreSQL and NoSQL databases, Stripe billing, webhook handling, and third-party integrations documented with OpenAPI. EtaFlex, a B2B last-mile SaaS I built, eliminated manual dispatch overhead and syncs Shopify, ERPs, and WMS in real time through event-driven n8n pipelines.
→ Generative AI Integration: AI features that work inside your product, not alongside it. RAG pipelines, document intelligence, AI chatbots, and natural language interfaces built on LangChain, OpenAI GPT-4, and Gemini. EdLoop, a K-12 AI SaaS I built, auto-generates personalized student feedback and surfaces district-wide insights. saving teachers hours of manual grading weekly with FERPA-compliant architecture.
→ AI Agents & Agentic Systems: Intent identified, routed, and executed. no human in the loop. Autonomous multi-agent workflows on LangChain, LangGraph, and CrewAI. PromptPay executes real bank transfers from a single GPT-4 natural language command with full compliance logic built in.
→ RAG Pipelines & Natural Language Interfaces: Your data shouldn't be locked away from the people who need it. Conversational interfaces over databases, documents, and knowledge bases using FAISS, Chroma, and LangChain. TALK2DATA lets non-technical teams query live PostgreSQL databases in plain English, zero SQL, zero developer dependency, zero waiting on reports.
→ Workflow Automation & API Integration: If your team is still doing manually what a pipeline should handle, that's expensive. n8n, Zapier, and Celery connecting CRMs, REST APIs, and SaaS tools into Python automation workflows that run without supervision. My LinkedIn outreach automation increased qualified leads by 4x and cut research time by 80%.
𝐅𝐨𝐮𝐫 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬. 𝐕𝐞𝐫𝐢𝐟𝐢𝐞𝐝 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬.
↳ EtaFlex: Eliminated manual dispatch overhead and reduced logistics costs for a B2B last-mile delivery platform, real-time fleet routing, multi-stop optimization, Shopify/ERP/WMS sync, and Stripe payouts. Built on MERN and n8n.
↳ EdLoop: Saved teachers hours of manual grading weekly, FERPA-compliant K-12 AI SaaS that processes student work, auto-generates personalized feedback, and surfaces district-wide performance insights. Built on LangChain, FastAPI, and GCP.
↳ PromptPay: "Send 12K to John" executes a real bank transfer, multi-agent system identifies intent, validates compliance, and processes the transaction end to end. Built on LangChain, GPT-4, and Django.
↳ TALK2DATA: Non-technical teams query live databases in plain English, no SQL, no developer dependency, no waiting. Built on LangChain, FastAPI, and PostgreS
Additional: BrokerBot AI (real estate voice agent, response time down 60%), PMO Handover Bot (RAG over Google Drive, onboarding cut by 50%), InvoiceIQ (96% multilingual invoice extraction, replaced a paid SaaS tool), Smart Clinic Reservation System, LinkedIn Outreach Bot, Jira Microservice Automation, Career Ustad, Sovereign Urdu LLM, and more.
𝐇𝐨𝐰 𝐈 𝐰𝐨𝐫𝐤:
Architecture before code. Every system is documented, maintainable, and designed to survive handoff. I flag problems before they get expensive and communicate without being chased.
𝐆𝐨𝐨𝐝 𝐟𝐢𝐭: Startups and SaaS businesses building real products, scalable backend systems, Generative AI integration, or production-grade API development that needs to work under real users and real data.
𝐍𝐨𝐭 𝐚 𝐟𝐢𝐭: Vague briefs where "let's explore what AI can do" is the entire scope.
𝐒𝐭𝐚𝐜𝐤:
Backend: Python, FastAPI, Django, Flask, MERN Stack, Go
AI/LLM: LangChain, LangGraph, CrewAI, OpenAI GPT-4, Gemini, Ollama, Hugging Face
Automation: n8n, Zapier, Celery
Vector DBs: FAISS, Chroma
Vision & OCR: GCP Document AI, OpenCV
Voice: Bland AI, VAPI
Scraping: Playwright, Firecrawl
Databases: PostgreSQL, MongoDB, Redis
Cloud: AWS ( EC2, Lambda) , GCP, Docker
Describe what you're building or what's broken, just the core problem. I'll reply within 3 hours with whether I've solved something like it and exactly how I'd approach yours.
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.
Receive Client Requirements
Collect all documents, use cases, tone preferences, and integration details needed to define a clear project scope.
Review and Confirm Scope
Review all client-provided materials and confirm the chatbot's goals, data sources, and expected features before development.