AI Saas Playbook

AI SaaS Master Playbook

DIRECTIVE: Execute with absolute precision. Architecture must be robust, scalable, and fault-tolerant.

1. Domain Convergence

• Intelligence: LangChain/LlamaIndex for deterministic agentic reasoning and tool execution.
• Resilience: Apache Kafka for strictly decoupled, high-throughput, event-driven processing.
• Monetization: Stripe for aggressive subscription gating and usage-based billing.

2. System Architecture

%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
    A[User Request / Prompt] --> B{Subscription Active?}
    B -- No --> C[Stripe Checkout Flow]
    C --> D[Stripe Webhook: payment_intent.succeeded]
    D --> E[(User DB: Upgrade Tier)]
    B -- Yes --> F[API Gateway / Auth]
    F --> G[Kafka Producer]
    G --> H((Kafka Topic: 'ai.tasks.incoming'))
    H --> I[LangChain AI Worker Node]
    I <--> J[LLM Provider / Tools]
    I --> K((Kafka Topic: 'ai.tasks.completed'))
    K --> L[WebSocket / SSE Broadcaster]
    L --> M[Client UI]

3. Core Orchestration Logic

import os
import json
from fastapi import FastAPI, HTTPException
from kafka import KafkaProducer
import stripe

app = FastAPI()
stripe.api_key = os.getenv("STRIPE_SECRET_KEY")
producer = KafkaProducer(
    bootstrap_servers='kafka:9092',
    value_serializer=lambda v: json.dumps(v).encode('utf-8')
)

@app.post("/api/v1/execute-agent")
async def execute_agentic_workflow(payload: dict):
    user_id = payload.get("user_id")
    prompt = payload.get("prompt")

    # 1. Monetization Gate
    customer = stripe.Customer.retrieve(user_id)
    if not customer.subscriptions.data:
        raise HTTPException(
            status_code=402, 
            detail="Payment required. Please upgrade your tier."
        )

    # 2. Event-Driven Handoff
    task_payload = {"user_id": user_id, "prompt": prompt, "status": "QUEUED"}
    producer.send('ai.tasks.incoming', value=task_payload)
    
    return {"message": "Agent workflow initiated. Await completion via WebSocket."}

# ---------------------------------------------------------
# Background Kafka Consumer & LangChain Worker (Conceptual)
# ---------------------------------------------------------
# def consume_and_process():
#     for msg in consumer('ai.tasks.incoming'):
#         agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
#         result = agent.run(msg.value["prompt"])
#         producer.send('ai.tasks.completed', value={"user_id": msg.value["user_id"], "result": result})