Production-Grade AI Platform

Multimodal AI Companion Platform

Creators Monetize AI Clones via Telegram + Web Dashboard

Full-stack AI system architecture built with Python, FastAPI, Django, Kubernetes, RAG + Vector DB + LLM inference for scalable creator monetization

View Architecture View Repository
8+
Microservices
K8s
Orchestration
RAG
AI Pipeline
Multi
Modal AI

System Overview

What It Does

  • Creators deploy AI clones of themselves with custom knowledge bases
  • Fans chat with clones via Telegram with persistent memory
  • RAG + Vector DB powers contextual, personalized responses
  • Creators monetize via subscriptions or usage-based billing
  • Web dashboard shows analytics, revenue, and clone performance
  • Kubernetes deployment ensures scalability and reliability

What This Proves

Real AI System Design

Cloud-Native Engineering

RAG Architecture

Production Readiness

Business Alignment

Platform Thinking

Why This Architecture Matters

Scalable by Design

Kubernetes HPA, GPU node pools, and microservices handle millions of conversations

Revenue-Optimized

Multi-tenant isolation, usage tracking, and Stripe integration for creator monetization

Observable & Fast

Prometheus metrics, Grafana dashboards, streaming responses, and cost tracking

Core Platform Architecture

Request Flow: Telegram User → AI Response

Telegram Users

Chat with AI clones

↓

Telegram Bot API

Webhook receiver

↓

FastAPI Gateway

Auth, Rate Limits, Routing

↓

Vector DB

Qdrant/Milvus

LLM Server

vLLM/Ollama

Memory Store

Postgres/Redis

↓

Response Streamed Back

To Telegram user

Creator Dashboard Flow

Creators

Manage AI clones

↓

Next.js Dashboard

React frontend

↓

Django Backend

Content, Analytics, Billing

↓

Content Upload

Analytics

Billing

Clone Settings

Tech Stack (MANDATORY)

Backend

  • Python: FastAPI + Django
  • PostgreSQL: Relational data
  • Redis: Sessions + rate limiting

AI Layer

  • vLLM/Ollama: Local inference
  • Qdrant/Milvus: Vector DB
  • Sentence Transformers: Embeddings

Frontend

  • React/Next.js: Web app
  • Tailwind CSS: Styling
  • Chart.js/Recharts: Analytics

Infrastructure

  • Kubernetes: Orchestration
  • Helm: Package management
  • NGINX Ingress: Load balancing

Observability

  • Prometheus: Metrics collection
  • Grafana: Dashboards
  • Sentry: Error tracking

Storage

  • MinIO: Object storage
  • PostgreSQL: User data
  • Redis: Caching layer

Backend Microservices on Kubernetes

Service Stack Responsibility
api-gateway FastAPI Auth, routing, rate limiting
bot-service FastAPI Telegram integration
ai-engine FastAPI RAG + LLM orchestration
creator-service Django Users, clones, content management
billing-service Django Stripe integration, subscriptions
frontend Next.js Creator dashboard UI
vector-db Qdrant/Milvus Embedding storage and search
llm-server vLLM/Ollama GPU inference engine

RAG Pipeline Architecture

Content Ingestion Flow

1

Creator uploads content

PDFs, YouTube links, text, voice samples

2

Django service stores metadata

File info, creator ID, content type

3

Content chunked + embedded

Sentence Transformers generate vectors

4

Stored in Vector DB

Under creator namespace for isolation

Query & Response Flow

1

Telegram query received

User asks clone a question

2

Embed question

Convert to vector representation

3

Vector similarity search

Retrieve top-k relevant chunks

4

Context injected into LLM prompt

Augmented generation with creator knowledge

5

Response streamed back

Real-time token-by-token delivery

Features by Role

👤 Creators

Create AI Clone

  • • Upload PDFs, YouTube links, text
  • • Voice samples (optional)
  • • Set personality style

Monetization

  • • Free / Paid / Token-based access
  • • Stripe subscription management
  • • Revenue tracking

Analytics

  • • Chat volume and retention
  • • Revenue metrics
  • • Top questions asked

🤖 Users (Telegram)

  • Discover AI clones in marketplace
  • Chat with clones via Telegram
  • Persistent conversation memory
  • Usage limits and tracking
  • Subscription paywall integration
  • Multimodal: text, voice, images

🧠 AI System

  • Full RAG pipeline implementation
  • Conversation memory embedding
  • Multi-tenant vector namespaces
  • Streaming LLM responses
  • Fallback models for failures
  • GPU autoscaling on K8s

Multimodal Support

Voice Messages

Telegram voice → Whisper STT → LLM → TTS → Telegram

Image Queries

OCR / Vision models → RAG → LLM → text/image response

Kubernetes & Observability

Kubernetes Architecture

Namespaces

dev staging prod

Horizontal Pod Autoscaling

  • • ai-engine - CPU-based scaling
  • • llm-server - GPU utilization
  • • api-gateway - Request rate

Infrastructure

  • • GPU node pool for inference
  • • Helm charts per microservice
  • • NGINX Ingress for routing
  • • Persistent volumes for storage

Observability

Prometheus Metrics

  • • LLM latency: P50, P95, P99
  • • Vector search time: Query performance
  • • GPU utilization: Resource optimization
  • • Cost per request: Financial tracking

Grafana Dashboards

  • • System health overview
  • • AI performance metrics
  • • Cost and revenue tracking
  • • User engagement analytics

Error Tracking

  • • Sentry for exception monitoring
  • • Real-time alerting
  • • Performance degradation detection

Security & Isolation

JWT Authentication

Secure API access with token validation

Multi-Tenant Isolation

Creator data namespaced in Vector DB

Rate Limiting

API gateway + Redis for abuse prevention

Demo Scenarios

1

Creator Onboarding

Create Clone

Sign up, set personality, choose name and avatar

Upload Knowledge

PDFs, YouTube, text content → chunked + embedded

Enable Monetization

Set pricing, connect Stripe, go live

2

User Chat Experience

Telegram Conversation

User starts chat, AI responds with context

Memory Recall

AI remembers previous conversations

Paywall Enforcement

Free trial ends → subscribe to continue

3

Admin Monitoring View

Revenue Dashboard

MRR, churn rate, top creators by revenue

Latency Metrics

LLM response time, vector search performance

Cost Tracking

GPU costs, inference usage, profit margins

Repository Structure

/ai-companion-platform
  /services
    /api-gateway          # FastAPI - Auth, routing, rate limiting
    /bot-service          # FastAPI - Telegram integration
    /ai-engine            # FastAPI - RAG + LLM orchestration
    /creator-service      # Django - Users, clones, content
    /billing-service      # Django - Stripe integration
  /frontend               # Next.js - Creator dashboard
  /helm                   # Helm charts for K8s deployment
  /infra                  # Terraform, K8s configs
  /docs                   # Architecture, API docs
  docker-compose.yml      # Local development setup
  README.md               # Project documentation
View Full Repository

Let's Discuss AI Platform Engineering

Interested in scalable AI systems, RAG architecture, or Kubernetes deployments? Let's connect.

Or reach out directly:

rahulgupta37079@gmail.com +91 9137361474