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Causify Blog#

  • in Business

Correlation is not Causation:-Wind Turbine Edition

Despite the power of modern machine learning, wind farm operators still struggle to predict with confidence when a turbine's gearbox is about to fail and plan maintenance before disaster strikes.

At Causify, in collaboration with a $60B global wind turbine operator, we've been developing AI-driven models to predict wind turbine failures before they occur. By leveraging Causal AI, we can now predict main bearing failures up to three weeks in advance, enabling real-time preventive maintenance and reducing costly downtime.

  • in Business

Causal AI is the Next Step of Predictive Analytics

Over the past decade, data analytics and big data have transformed the way organizations operate. Companies have invested heavily in collecting, organizing, and analyzing massive volumes of data. Dashboards, predictive models, and automated reports are now commonplace, helping decision-makers see trends, forecast outcomes, and optimize processes.

But despite these advances, data analytics, big data, and (traditional) AI have their limits, and those limits are becoming increasingly clear.

  • in Business

Causal News:-Causal AI Market Research

Causal AI is the science of understanding not just what happens, but why it happens and how to change it. Unlike traditional AI models which focus on correlations, causal AI emphasizes cause-and-effect relationships, enabling deeper insights, better decision-making and what-if simulation.

According to Causal AI report, the global causal AI market was valued at approximately USD 40.55 billion in 2024, and is projected to reach USD 757.74 billion by 2033, expanding at a CAGR of ~39.4% from 2025 to 2033.

  • in Devops

Docker Executables:-No More Install Guides

Have you ever spent hours trying to install a tool, only to hit version conflicts or mysterious errors? Do you need to run the same tool on different computers (e.g., Mac vs Linux laptops vs AWS dev servers vs CI servers), having problems maintaining the tools aligned, or even just building?

At Causify, we face this problem repeatedly with different developer utilities. To solve it, we introduced a toolchain pattern we call Dockerized Executables.

What’s the ETA?

“What’s the ETA for feature XYZ?”
“When will feature XYZ be done?”

Last time I counted, I've been asked and I've asked those questions about 14,400 times in 20 years I've spent in the coding trenches.

Why Causal AI is the Future of Automated Decision-Making

Over the past decade, organizations have poured immense effort into building systems that harness the power of big data and traditional analytics. These systems, spanning from dashboards to predictive models, have helped analyze historical data, detect patterns, and forecast future events. However, there’s a fundamental problem with this approach: it stops at correlation. It can tell you what is likely to happen, but not why.

That’s where Causal AI comes in.

Cracking the Long Tail

For the past decade, the AI and data science industry has been shaped by the dominant narrative of big data. From ad-tech to recommendation systems, the idea has been simple: more data leads to better models, so companies should focus on big data, scaling their infrastructure, deep learning, and tooling.

But here’s the truth: big data problems are extremely rare. Furthermore, big data problems are "easy" problems, in the sense when data is abundant and clean, most machine learning models perform well. The problem with big data is on the tools and compute.

AI for Optimal Decision-Making

In the age of AI, every business leader hears the same message: use data to make better decisions. But most systems today still make brittle, black-box predictions without understanding the why behind the what. The reality? We’re still in the early innings of AI-powered decision-making, and the biggest unlocks lie ahead.

At Causify, we believe businesses could be 10x more efficient in revenue, profit, and risk reduction by making every decision rationally, probabilistically, and causally. Here's why.