Cycling analytics, reimagined
Predict
performance.
Not potential.
Divergence applies machine learning to your physiological data — surfacing insights that tell you not just how you rode, but how you will ride.
Connects to Garmin, Wahoo & Strava
Heat Adaptation — last 90 days
ML model active
FTP (estimated)
418w
Heat Adaptation
72/100
Recovery score
39/100
Form index
109
Model detecting upward trend — updated daily from connected devices.
Platform
Built for riders who need
answers, not more charts.
Every feature is oriented around a single question: what should you do next to get faster?
Predictive Power Modelling
A model trained on your ride history forecasts where your functional threshold is heading — and what's driving the change.
Physiology Fingerprinting
Your heart rate response, power curve shape, and fatigue signature are unique. Divergence builds a model of you, not a population average.
Race & Event Simulation
Drop in a course file. Get a lap-by-lap pacing model, projected energy expenditure, and likely fade points — before you pin on a number.
Recovery Intelligence
Multi-signal recovery scoring that blends training stress, sleep quality, and HRV into a single, actionable daily readiness score.
Adaptive Training Zones
Zones recalculated from your actual recent performance — not anchored to a single test you did six months ago.
Segment Benchmarking
Understand exactly where you gain and lose time on climbs relative to your own baseline, with power-to-gradient breakdowns.
ML engine
A model that learns
you.
Divergence's inference pipeline ingests raw sensor streams and constructs a personalized physiological model — updated continuously, not on a quarterly test schedule.
- Personalized, not generic
The model weighs your own history more heavily than population-level priors. Accuracy improves as your ride log grows.
- Uncertainty-aware
Every prediction includes a confidence bound. Divergence tells you when to trust the model and when to trust your legs.
- Explainable outputs
Feature attribution shows which signals drove each prediction. No black boxes, no inexplicable numbers.
- Continuous learning
The model updates after every activity — not on a quarterly test cycle. Your current form is always reflected.
Inference pipeline
Transformer-based architecture. SHAP attribution on every prediction.
Integrations
Meets you where
your data already lives.
Divergence connects to the devices and platforms you already use. No manual uploads, no data gaps.
Garmin Connect
Device & platform
Wahoo ELEMNT
Device
Strava
Activity platform
Polar Flow
Device & platform
TrainingPeaks
Training platform
Oura Ring
Recovery & sleep
Whoop
Recovery & sleep
FIT file import
Universal format
More integrations in development.
Early access
Your next breakthrough
starts with the data you already have.
Divergence is currently in private development. Request access and we'll be in touch when a spot opens.
No spam. No marketing cadence. Just a note when we're ready for you.