Most IoT platforms start with a simple promise.
Connect devices. Collect data. Show dashboards.
That promise served the industry well for years.
But something has been missing.
As more devices come online and data volumes grow, static rules and fixed thresholds become limiting. Alerts trigger too late. Patterns remain hidden. Decisions still rely heavily on human interpretation rather than learned behaviour from the data itself.
Over the past year, we have been asking a different question inside Favoriot.
What if IoT systems could learn from their own data
What if behaviour could be anticipated, not just observed
What if intelligence were part of the platform, not an external add-on
That line of thinking has led us to a significant internal shift.

Favoriot is preparing to take a step beyond traditional IoT.
Not by changing how devices connect.
Not by replacing dashboards or rules.
But by introducing learning capabilities directly into the heart of the platform.
This upcoming evolution reflects how connected systems are being used today and how they will be expected to behave tomorrow. Systems that do more than report the past. Systems that recognise patterns, adapt over time, and support better decisions as conditions change.
We are not ready to share the complete details yet.
What we can say is this.
Favoriot is moving toward a model where intelligence grows alongside data.
Where insight is not pre-configured, but discovered.
Where IoT begins to feel less reactive and more aware.
More will be shared soon. Just wait for the news here on Monday, December 29, 2025.
For now, consider this a heads-up.
Something fundamental is being added to Favoriot.
And it changes how connected data can work for you.
FAVORIOT Intelligence References
- Official Announcement -Favoriot Evolves into an AIoT Platform with Built-in Machine Learning
- Why Favoriot Chooses Machine Learning Over LLMs
- Machine Learning vs Deep Learning in IoT
- Traditional Machine Learning vs Large Language Models (LLMs)
- Why Favoriot’s Built-in Machine Learning Matters for AI Researchers and IoT Developers
- Why Favoriot’s ML Infrastructure Reduces Costs
- AI and ML in IoT, Explained Without the Jargon
- Start Anomaly Detection in IoT with Less Data
- AI and ML in IoT, Explained Without the Jargon
- The Key Differences: Favoriot’s Rule Engine 2.0 and AI Agents
- The Role of Machine Learning in IoT Systems
- Favoriot’s Rule Engine 2.0: A Structured Approach to IoT Automation
- A Quiet Shift Is Coming to the Favoriot Platform






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