Every insulin recommendation is calculated from three independent components:
A deep dive into Bayesian learning, ISF adaptation, 5-layer safety validation, and personalized insulin delivery.
Dossi calculates insulin in three components based on current physiology and glucose prediction.
Every insulin recommendation is calculated from three independent components:
Estimated carbs ÷ Insulin-to-Carb Ratio (ICR)
Based on grams of carbohydrates you log or estimate.
(Current Glucose − Target) ÷ Insulin Sensitivity Factor (ISF)
Brings high glucose down or avoids low glucose.
Insulin on Board already working in your body
Recommendation rounded to nearest 0.05 units
Matches Omnipod precision. Then validated through 5 safety layers.
Your ICR and ISF don't stay static. Dossi learns from your glucose outcomes and adjusts these factors automatically. After you manually enter insulin and see how your glucose actually responds, Dossi records the pattern and learns whether you need more or less sensitivity in that context.
Before any insulin is delivered, the dose passes through rigorous validation layers. At ANY point, if conditions fail, Dossi blocks or warns.
If prediction data is available, Dossi analyzes whether the proposed dose risks low glucose in the next 2 hours. May automatically reduce dose if hypo risk is high. Uses prediction uncertainty to stay conservative.
TOCTOU Protection: Before delivering insulin, the system immediately re-validates with fresh glucose data. This prevents the case where glucose changed between recommendation and delivery—a critical "time-of-check to time-of-use" safety pattern.
Dossi learns how each factor affects your insulin needs and adjusts your ISF and ICR automatically.
Poor sleep increases insulin resistance.
Insulin sensitivity varies predictably throughout your day.
Absorption slows as your infusion site gets older.
Different body locations absorb insulin at different rates.
Workouts increase insulin sensitivity immediately and for hours afterward.
Hormonal cycles change insulin sensitivity significantly.
Fat and protein slow glucose absorption compared to carbs alone.
Caffeine consumed without food can affect glucose stability.
Observable factors: Fat intake, fiber intake, and illness type are logged and analyzed for insights, but don't directly adjust your ISF/ICR. Prediction confidence only: Seasonal patterns and circadian regimes affect prediction uncertainty bounds, helping Dossi know when to be more conservative with recommendations.
After every meal, Dossi compares its prediction to reality and refines your personal model.
The Learning Loop:
Model your expected glucose 2–4 hours out.
Track your actual glucose outcome.
Determine which active factors explain the difference.
Refine your personal ISF/ICR model.
Learning only applies when: (1) you've accumulated 10+ observations of a pattern, (2) pattern shows 50%+ confidence, (3) you have 14+ days historical data, and (4) Smart Mode is enabled. Paused during post-low recovery, illness, and sensor warmup.
Learned Adjustment Bounds: All learning is capped. Adjustments can only modify your base settings by ±30%. Final ISF can only vary between 0.6× and 1.6× your entered value. This prevents runaway learning from corrupting dosing even if data quality degrades.
Dossi shows you how confident it is in each recommendation.
Good CGM signal, stable context, no conflicting factors. Trust the recommendation.
Some contextual noise or adjustments being applied. Use judgment.
Poor CGM data, unusual context, or insufficient historical data. Consider manual adjustment.
You make the final bolus decision. Biometric auth required. Dossi recommends; you decide.
When in doubt, Dossi suggests less insulin, not more.
Works in Calibration Mode with just your pump settings—no learning required.
Every dosing decision is logged with full reasoning for review.
Based on published diabetes research, ADA guidelines, and Loop literature.
If learning would break bounds, it's clamped visibly—never silently modified.
Open-source code. You decide what gets deployed to your device.