Autonomic Intelligence
How our data, neuroscience and machine learning work together
Autonomic follows how an individual's focus, mood, energy, sleep and stress change over time. Users complete a roughly three-minute daily check-in, and the platform builds a longitudinal picture of that individual rather than comparing a single observation against a generic population.
For individuals, those insights shape personalized daily actions. For organizations, privacy-protected aggregate trends help leaders turn those insights into lasting performance practices.
Our machine-learning models were developed using Autonomic's own longitudinal dataset, collected over five years from people using the platform. The dataset now contains more than 350,000 records. The models learn relationships between repeated measures, behaviours, interventions and outcomes over time.
Targets
Inputs
*Objective and multimodal signals are progressively being integrated into Autonomic's intelligence layer.
The Autonomic data loop
Every interaction can capture three connected layers
01 · Signal
Behavioral and physiological indicators associated with brain performance
02 · Decision
What was assessed, recommended or assigned, including validated human decisions
03 · Outcome
How the individual's signals and behaviour changed following the decision
The model identifies patterns in an individual's longitudinal data. Autonomic's decision intelligence determines what is relevant and which approved response is appropriate, based on confidence thresholds, safety rules and the individual's history.
Recommendations are drawn from Autonomic's proprietary neuroscience knowledge system, which translates established research into approved insights, behavioural strategies and evidence-based actions. Human oversight governs adaptation and provides an additional layer of validation and safety.
Autonomic has several different things happening:
01
What an individual reports subjectively or is captured objectively.
02
What patterns Autonomic identifies.
03
How Autonomic determines what information/action may be appropriate.
04
The neuroscience-based habit/action/message delivered.
05
Where trained validators oversee/adjudicate outputs.
Study 1
+29.8%
Sleep
+25.1%
Mood
−18.0%
Stress
+12.7%
Focus
+10.0%
Energy
All five changes were statistically significant (p < .001).
Because this was a nonrandomized study, the findings demonstrate significant improvements associated with Autonomic use, but do not by themselves establish causation.
Study 2
+24.78%
Focus
+19.32%
Energy
+15.39%
Sleep
−23.6%
Cortisol
−7.9%
Frontal theta during working memory
−13.6 ms (−3.3%)
P300 latency
Focus and energy improved significantly over time, and sleep showed a significant linear trend.
The EEG findings showed significantly reduced frontal theta during working memory and significantly faster P300 latency, consistent with reduced cognitive effort and faster information processing during the task.
Cortisol decreased by 23.6%, but the change was not statistically significant. We report it as an observed biomarker change, not as evidence of a confirmed cortisol effect.
Our evidence is strong within the populations we have studied. We do not assume those findings automatically generalize to everyone. Autonomic has been scientifically studied in university populations, with published research demonstrating measurable changes across behavioural, physiological and neurophysiological measures.
We have not yet established Autonomic's efficacy in:
We are continuing to expand the populations we study, investigate which components of Autonomic contribute to observed outcomes, and evaluate how objective measures such as EEG, HRV, facial movement, voice, and passive signals can strengthen personalization over time.
Where the evidence is established, we will say so.Where it is still emerging, we will say that too.
