You will get a custom AI chatbot using FastAPI
Project details
I build custom AI Agent RAG Bots and chatbots using FastAPI or Django. My focus is on small to mid-sized projects where I can deliver clean backend code, document-based search (RAG), and reliable cloud deployment. Each package includes source code, deployment, and revisions so you get a working solution you can use right away.
Programming Languages
PythonWhat's included
| Service Tiers |
Starter
$80
|
Standard
$150
|
Advanced
$250
|
|---|---|---|---|
| Delivery Time | 3 days | 8 days | 15 days |
Number of Revisions | 2 | 4 | 6 |
Number of Pages | 2 | ||
Design Customization | - | - | - |
Content Upload | - | - | - |
Responsive Design | - | - | - |
Source Code |
Frequently asked questions
About Adarsh
Backend & AI | Django, FastAPI, LangChain, ML Deployment, Cloud
Burhar, India - 7:37 am local time
Backend Development (Django & FastAPI)
– REST APIs with secure JWT authentication
– Microservices with FastAPI for agentic AI systems
– PostgreSQL (Neon, Supabase, GCP Cloud SQL), Redis for chat history
– Minimal AI-generated frontend for Django monoliths (if required)
AI & Agentic Systems
– LangChain & LangGraph-based RAG bots and conversational agents
– Multi-agent orchestration (context-aware, retrieval-driven systems)
– Domain-specific assistants (tutoring, legal, custom knowledge bases)
ML/DL Model Deployment
– Deploy ML/DL models (CNNs, classification, NLP) with Docker + FastAPI
– Cloud deployment on Google Cloud Compute Engine, Render
– Custom domain setup, HTTPS, CI/CD pipelines for auto-updates
– Integration with GCP Storage / R2 for model & data persistence
What You Get Working With Me
✅ End-to-end backend & AI system (no frontend needed)
✅ Cloud-native deployment & monitoring
✅ Clean, well-documented, production-ready code
✅ Focus on scalable and maintainable architecture
If you need a backend-first engineer who can deliver AI-powered, cloud-deployed solutions, let’s talk.
Steps for completing your project
After purchasing the project, send requirements so Adarsh can start the project.
Delivery time starts when Adarsh receives requirements from you.
Adarsh works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering
I will review your documents, use case, and deployment preferences (Render, Cloud Run, or Compute Engine).
Environment Setup
I will set up the backend with FastAPI or Django, database (if required), and necessary libraries (LangChain, LangGraph, Redis, etc.).