Name: Automate Smart Factory IoT - Framework to Production
Author: Randeep Bhatia

The Problem

On Monday you tested the 3 prompts in ChatGPT. You saw how sensor data extraction → anomaly detection → alert generation works. But here's the thing: you can't have operators manually checking dashboards and running prompts for 500 sensors. One engineer spending 4 hours per day monitoring sensor data manually? That's $120/day in labor costs. Multiply that across a factory floor and you're looking at $36,000/year just on reactive monitoring. Plus the 30-minute lag time between anomaly and alert means equipment failures you could have prevented.

4+ hours

Per day manually monitoring sensors

30 min lag

Between anomaly and manual detection

Can't scale

Beyond 10-20 sensors per operator

See It Work

Watch the 3 prompts chain together automatically. This is what you'll build.

Watch It Work

See the AI automation in action

Live Demo • No Setup Required

The Code

Three levels: start simple, add reliability, then scale to production. Pick where you are.

Basic = Quick startProduction = Full featuresAdvanced = Custom + Scale

Simple API Calls

Good for: 10-100 sensors | Setup time: 30 minutes

Simple API Calls

Good for: 10-100 sensors | Setup time: 30 minutes

# Simple IoT Sensor Monitoring (10-100 sensors)
import openai
import json
import os
from datetime import datetime
from typing import Dict, List, Optional

# Set your API key
openai.api_key = os.getenv('OPENAI_API_KEY')

def monitor_sensor_data(sensor_data: str) -> Dict:
    """Chain the 3 prompts: extract → detect anomalies → generate alerts"""
    
    # Step 1: Extract and structure sensor data
    extraction_prompt = f"""Extract IoT sensor data and format as JSON.

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When to Level Up

Simple API Calls

Basic extraction → analysis → alert chain
Manual MQTT subscription
Local logging
No historical context

Level Up

Add Reliability Layer

Retry with exponential backoff
Redis rate limiting
InfluxDB time-series storage
MQTT auto-reconnect
Historical data queries
Structured logging

Level Up

Multi-Agent System

LangGraph orchestration
ML + LLM hybrid analysis
Distributed processing
Real-time dashboards
Advanced alerting (PagerDuty, Slack)
Predictive maintenance scoring

Level Up

Enterprise Platform

Kubernetes orchestration
Auto-scaling based on load
Multi-region deployment
Custom ML model training
Advanced analytics dashboard
API for third-party integrations
Compliance reporting
Cost optimization

Manufacturing Tech Gotchas

Real challenges from production deployments. Learn from others' mistakes.

MQTT Message Ordering

Use MQTT QoS 2 for critical sensors. Add sequence numbers and timestamps to messages. Implement buffer window for reordering.

Solution

# MQTT QoS 2 with sequence tracking
import paho.mqtt.client as mqtt
from collections import deque
import time

class OrderedMessageBuffer:
    def __init__(self, window_size=10, timeout=5):
        self.buffer = deque(maxlen=window_size)

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OPC UA Data Type Mapping

Normalize OPC UA data to flat JSON before sending to LLM. Handle null values explicitly. Use type hints.

Solution

# OPC UA to LLM-friendly JSON
from opcua import Client
import json
from typing import Any, Dict

def normalize_opc_value(value: Any) -> Any:
    """Convert OPC UA value to LLM-friendly format"""
    if value is None:

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Time-Series Data Volume

Aggregate historical data before sending to LLM. Use statistical summaries (min, max, mean, std). Only send anomalies in detail.

Solution

# Time-series data aggregation
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

def aggregate_historical_data(
    sensor_id: str,
    hours: int = 24,

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Alert Fatigue from False Positives

Implement alert scoring and suppression. Use LLM to validate ML anomalies against domain knowledge. Batch low-priority alerts.

Solution

# Alert scoring and suppression
from dataclasses import dataclass
from typing import List, Dict
import asyncio

@dataclass
class Alert:
    sensor_id: str

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Cost Management at Scale

Implement tiered processing: ML for screening, LLM only for anomalies. Cache common patterns. Use cheaper models for validation.

Solution

# Cost-optimized processing pipeline
from enum import Enum
import hashlib

class ProcessingTier(Enum):
    ML_ONLY = 1  # $0.001/call
    CHEAP_LLM = 2  # $0.005/call (GPT-3.5)
    PREMIUM_LLM = 3  # $0.02/call (GPT-4)

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Adjust Your Numbers

Daily Volume (competitors)500

105,000

Manual Time per analysis (minutes)5 min

1 min60 min

Staff Hourly Rate ($)$50/hr

$15/hr$200/hr

❌ Manual Process

Time per analysis:5 min

Cost per analysis:$4.17

Daily volume:500 competitors

Daily:$2,083

Monthly:$45,833

Yearly:$550,000

✅ AI-Automated

Time per analysis:~2 sec

API cost:$0.02

Review (10%):$0.42

Daily:$218

Monthly:$4,803

Yearly:$57,640

You Save

0/day

90% cost reduction

Monthly Savings

$41,030

Yearly Savings

$492,360

💡 ROI payback: Typically 1-2 months for basic implementation

🏭

Want This Running in Your Factory?

We build custom smart factory AI systems that integrate with your existing MQTT/OPC UA infrastructure. From 10 sensors to 10,000, we handle the complexity so you focus on manufacturing.

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Automate Smart Factory IoT 🚀

The Problem

See It Work

Watch It Work

The Code

Simple API Calls

When to Level Up

Simple API Calls

Add Reliability Layer

Multi-Agent System

Enterprise Platform

Manufacturing Tech Gotchas

MQTT Message Ordering

OPC UA Data Type Mapping

Time-Series Data Volume

Alert Fatigue from False Positives

Cost Management at Scale

Adjust Your Numbers

❌ Manual Process

✅ AI-Automated

You Save

Want This Running in Your Factory?