Plan a pilot 
Preparing the reasoning experience…
Active reasoning, grounded in the free energy principle. Norlo connects enterprise knowledge with live evidence to guide what to learn or do next.
Discover active reasoningResearch origins at Deakin University · A²I²
One situation can have several explanations. Keep them in view until the evidence tells them apart.
A customer is considering leaving.
The next question should do more than continue the conversation. It should reveal something that matters.
“What’s making you consider leaving?”
A question chosen for what it could help distinguish.
Active inference considers both what an action could achieve and what it could help you learn.
Information gainWhich action could reduce uncertainty?
Preferred outcomesWhich action could help reach the goal?
Expected free energy connects these two considerations.
As evidence arrives, revise the explanations—and the next action. Reasoning is a loop, not a one-off answer.
“My support issue still isn’t resolved.”
Every customer conversation. Every service case. Every decision made with incomplete information.
Norlo’s ambition is to bring a reasoning layer into these moments: connecting shared knowledge, live evidence and a useful next step.
Try a different conversation, support issue or service case. Add an observation and see the reasoning change.
Interactive illustration · Predefined examplesDecision workspaceLikely explanations
Illustrative probabilities. Scripted example, not calibrated model outputs.
What does the customer say next?
Choose an observation to update the next action.
A research foundation for connecting belief, uncertainty and action.
Deakin University · A²I²To act well, a system needs a model of what’s happening—and a way to revise it.
The free energy principle describes adaptive systems in terms of minimising variational free energy, a mathematical bound on surprise. Active inference applies this framework to belief updating and action.
Use observations to revise a model of the situation. Variational free energy provides an objective for this inference.
Evaluate possible actions using expected free energy, considering both information gain and preferred outcomes.
For Norlo, the practical idea is clear: use evidence to understand a case, then recommend what is useful to learn or do next.
The papers explain the theoretical framework. They do not establish Norlo product performance or institutional endorsement.
The brain is a visual metaphor for the reasoning loop. Highlights are illustrative and do not map Norlo functions to anatomical regions.
Start with a bounded workflow. Define the decision, the available evidence and how your team will judge a useful recommendation.