From Alarm Fatigue to Real-Time Insights: A Case Study in AI-Powered Industrial Observability

One-liner summary:
ControlRooms.ai partnered with Ideas2IT to build an AI-powered observability platform that ingests terabytes of sensor data, detects anomalies in real-time, and helps plant operators troubleshoot before production is impacted.
Industry
Manufacturing
Location
Austin, Texas
Tech Stacks

Frameworks/Platforms:

  • Istio Service Mesh 
  • Azure ML 
  • Azure Functions 
  • Azure Event/IoT Hub 
  • Tusd

Tools/Integrations:

  • Ably 
  • Sendgrid 
  • Sentry 
  • Intercom 
  • Terraform 
Challenge

Billions are lost each year to downtime that could have been prevented. The goal: make predictive analytics practical, fast, and operator-ready.

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The Problem with the Status Quo

Industrial manufacturers lose billions annually to unplanned downtime. Operators often sift through thousands of trends manually and respond only after alarms are triggered. ControlRooms.ai set out to change that by building a cloud-native platform that detects issues before human intervention is needed, using machine learning and continuous data ingestion.

With early traction and fresh funding, they had the vision in place. What they needed was a technology partner to deliver on it.

Where the Gaps Were

To make predictive analytics usable by operators,the platform needed to handle significant complexity:

  • Stream terabytes of sensor data from IoT devices in near real time

  • Detect and surface anomalies proactively with minimal false positives

  • Trigger notifications across channels (web, Teams, email)

  • Support a multi-tenant architecture for multiple plants and customers

  • Stay cost-efficient enough to roll out commercially

Commercial cloud platforms could not meet the performance and customization demands. So we built a custom solution from the ground up.

What We Delivered

Ideas2IT designed and implemented a complete observability platform purpose-built for industrial use.

Key Moves:

  • Built a real-time ingestion pipeline using Azure IoT Hub → Event Hub → Stream Analytics

  • Deployed AI models (Random Forest + Variational Autoencoder) via Azure ML + AKS for anomaly detection

  • Developed a fast, resilient backend using FastAPI microservices in Python

  • Created a visual interface in React and D3 for trend analysis and KPI monitoring

  • Embedded a robust rule engine (Celery) for real-time alerting via Ably, Sendgrid, and Teams

  • Architected a secure, multi-tenant setup using TimescaleDB, Istio, Cognito, and Key Vault

  • Optimized ML pipelines for 70% cost reduction and 400% faster ingestion

The platform was fully containerized, monitored with Datadog, Grafana, and Loki, and designed for scale.

Outcomes We Achieved

KPI Result
File ingestion speed 400% faster
ML deployment cost 70% lower
Infra cost 300% reduction via smarter multi-tenancy
Historical data query time <300ms
Time-to-insight From minutes to seconds

Operators can now detect issues before alarms are triggered. Engineers troubleshoot faster. The ControlRooms.ai team onboards new customers in days, with confidence in production readiness.

Industry
Manufacturing
Location
Austin, Texas
Tech Stacks

Frameworks/Platforms:

  • Istio Service Mesh 
  • Azure ML 
  • Azure Functions 
  • Azure Event/IoT Hub 
  • Tusd

Tools/Integrations:

  • Ably 
  • Sendgrid 
  • Sentry 
  • Intercom 
  • Terraform 
Challenge

Billions are lost each year to downtime that could have been prevented. The goal: make predictive analytics practical, fast, and operator-ready.

‍

Key Takeaways

  1. Sensor data is only useful when delivered in real time and with context

  2. Machine learning models must be production-grade, including validation and alerting

  3. Multi-tenancy is a design requirement

This platform was designed to scale across plants, sectors, and use cases with industrial-grade resilience.

Co-create with Ideas2IT

We show up early, listen hard, and figure out how to move the needle. If that’s the kind of partner you’re looking for, we should talk.
We’ll align on what you're solving for - AI, software, cloud, or legacy systems
You'll get perspective from someone who’s shipped it before
If there’s a fit, we move fast — workshop, pilot, or a real build plan
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Manufacturing