March 3, 2026 | Blog
The power of Acutro’s Dynamic Logic Engine (dylo)
Written by: Alex Hribsek, Engineering Manager
Smart buildings generate massive amounts of data. Sensors track temperatures and occupancy, energy meters monitor consumption, weather services provide environmental context, and asset monitoring systems calculate equipment health. But data alone doesn’t create intelligence. The real value comes from understanding relationships between signals and acting on them.
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Virtual variables (VVs)
That’s exactly what Acutro’s Dynamic Logic Engine (dylo) is designed to do.
Dylo is the computational core of the platform—an advanced logic evaluation engine that transforms raw IoT signals into meaningful, actionable intelligence through Virtual Variables (VVs).
Turning data Into intelligence with virtual variables
Virtual Variables are the fundamental building blocks of dylo. Think of them as smart signals that derive meaning from other data.
Within the platform, VVs currently exist in several forms, including rules and virtual points. Each VV contains logic that is continuously evaluated by the engine, producing dynamic outputs that reflect the real-time state of the building.
Instead of simply collecting data, dylo allows buildings to interpret their own behaviour.
Flexible logic for real-world complexity
Buildings are complex systems. Conditions change constantly, and simple thresholds rarely tell the whole story. Dylo addresses this complexity by supporting two types of logic:
Non-conditional logic handles calculations and expressions that combine multiple data sources.
Conditional logic introduces decision-making through if, else if, and else structures. This allows rules to react dynamically to changing conditions.
For example, a rule might detect when energy consumption rises above expected levels during operating hours, but ignore similar patterns overnight. This kind of contextual intelligence is what turns raw data into operational insight.
The engine even supports nested conditions, enabling highly sophisticated logic structures when needed.
Dylo’s capabilities
A powerful library of functions
Dylo’s capabilities come from its flexible set of built-in functions.
Platform functions connect directly to the core data sources within the system:
- Points (sensors)
- Virtual points
- Asset health scores
- Energy meters
- Meteorological metrics
These functions allow dylo to evaluate both internal building signals and external environmental data.
On top of this, dylo supports standard mathematical functions such as addition, subtraction, multiplication, and division.
It also includes powerful aggregation functions:
- Minimum
- Maximum
- Average
- Sum
- Count
These allow engineers and operators to evaluate groups of sensors, equipment, or zones in meaningful ways.
Perhaps most importantly, dylo is designed for extensibility. When new use cases emerge, new functions can be added to the engine easily—ensuring the platform evolves alongside the buildings it serves.
Composable logic
Where the magic happens
One of dylo’s most powerful features is its ability to combine functions freely within mathematical expressions.
For example:
average(point(1), virtualPoint(2)) + meter(3) – meteorologicalMetric(4)
This type of expression can blend sensor readings, derived signals, energy consumption, and weather context into a single calculated value.
In practice, this means engineers can model complex building behaviours without writing traditional software. Dylo provides the flexibility of programming, but within a structured and accessible logic framework.
Intelligent Conditions and Comparisons
Dylo supports a full range of comparison operators:
- <
- <=
- =
- !=
- >
- >=
These comparisons can be combined using logical operators like AND and OR, allowing multiple conditions to be evaluated simultaneously.
This enables rules that reflect real-world operational scenarios—for example detecting when multiple factors combine to create inefficiency or risk.
Understanding duration with persistence
Many operational issues aren’t about a single moment—they’re about how long something lasts.
Dylo’s persistence function measures exactly that. It can evaluate how long a condition remained true, or what percentage of time it occurred. This works for both continuous and non-continuous events.
For building operators, this provides deeper insight into patterns such as recurring faults, sustained inefficiencies, or intermittent anomalies.
Context-aware logic with time windows
Buildings follow schedules, and dylo understands that context matters.
With time context, logic can be evaluated only during specific time windows within each day of the week. Multiple windows can be defined for each day, ensuring rules operate only when they’re relevant.
For example, comfort monitoring might run during working hours, while energy optimisation runs overnight.
The intelligence layer of the smart building
Acutro’s Dynamic Logic Engine transforms a Smart Building Platform from a passive monitoring system into an active intelligence layer.
By combining sensor data, asset health insights, energy metrics, and environmental context, dylo enables buildings to understand their own performance—and respond accordingly.
In a world where buildings are becoming increasingly connected, dylo provides the logic, flexibility, and scalability needed to unlock the full potential of IoT-driven infrastructure.
Because the smartest buildings don’t just collect data. They think with it.
