Because IOB should be easy to find
Active insulin is part of the decision context. Keeping IOB visible helps the user review the situation they are actually considering.
How it works
From scattered context to a decision trail you can review.
MDI decisions rarely depend on one number. Bolus Master brings the surrounding context into view, shows how an estimate was assembled, records what actually happened, and helps you look back at what followed.
Why use Bolus Master?
Because the useful part is what you can understand later.
Because a calculation should be inspectable
The user can see the mathematical components of the estimate instead of receiving an unexplained result.
Because plans change
The app preserves what the user reports actually doing, even when it differs from the original estimate.
Because one event is not the whole story
History and retrospective review help users compare events and identify questions worth exploring.
The four-part decision journey
A guided path from context to a better question.
Each part gives the user something useful in the moment and something worth revisiting later. The estimate, the logged action, the reminder, and the observation remain distinct.
01
Bring the decision into view
See the context that is easy to lose.
Confirm glucose and trend from a connected CGM when available, or enter them manually. Add carbohydrates, meal timing, exercise, and the active user-configured context before reviewing an estimate.
IOB stays visible
Start with insulin on board beside glucose, trend, and meal context—not as a later lookup.
Glucose + trendCarbohydratesMeal timingIOBExercise / context
In the momentFor confidence
02
Understand the estimate
See how the math was assembled.
Review the visible components before acting. The calculation logic can stay collapsed for a clean page or expand intentionally so the user can inspect the mathematical breakdown rather than accept an unexplained result.
IOB stays visible
Keep active insulin visible as a component of the context while inspecting how the estimate was assembled.
Inputs remain visibleSettings remain inspectableComponents stay distinctEstimate ≠ action
For confidence
03
Record what actually happened
Keep the record honest when plans change.
Log what the user actually took or did, even when it differs from the estimate. Meals can use None, Pre-bolus, Split dose, or Pre + split; correction-only entries use None, so no meal timer applies.
IOB stays visible
Record the actual dose and timing so the insulin-on-board context is easier to revisit rather than reconstruct from memory.
Dose actually loggedTiming and remindersExercise entryNotes and context
In the momentFor later
04
Look back and learn
Turn records into better questions.
Review one event, compare similar events, or examine progress over time. Repetition may reveal an association worth exploring, but a single observation does not establish a pattern or prove why an outcome occurred.
IOB stays visible
Return to the recorded IOB context when reviewing later outcomes; it remains part of the trail, not proof of cause.
Single-event reviewRepeated-pattern reviewProgress over timeAI-assisted organization
For later
The decision trail stays distinct
The four steps keep inputs and settings, the estimate reviewed, the action logged, and the later outcome connected without pretending they are the same thing. An estimate is not a logged action, and an observation is not proof of causation. Personal settings, treatment values, and outcome records are not shared on this public website.
Why IOB stays visible
Active insulin belongs in the context you review.
IOB is not an afterthought.
Bolus Master is shaped around the practical question that follows an earlier dose: what insulin on board is part of the context now? The app keeps logged insulin activity visible alongside the current inputs so the user can inspect the information present before reviewing an estimate.
This visibility supports review and logging. It does not guarantee that a record is complete, prevent an emergency, or replace an established care plan.
Transparent by design
A result is easier to question when you can see its components.
The estimate is based on the inputs and settings supplied by the user. Bolus Master keeps the mathematical breakdown available for intentional inspection, without presenting the estimate as an instruction or hiding the distinction between a calculation and an action.
Calculation componentsThe review can show the visible pieces that contributed to the estimate.Expandable logicThe user can open the detail intentionally instead of receiving a wall of information.Estimate ≠ actionThe estimate remains separate from what the user ultimately records.
Estimate reviewInspect
Current inputsGlucose, trend, carbohydrates, timing, exercise, and context entered for this review.
Active settingsThe user-configured settings and pattern context used by the calculation.
IOB contextLogged insulin activity that may still be part of the situation being reviewed.
Calculation logicThe transparent component breakdown available before logging.
Review before logging
The user remains the decision-maker.
Retrospective learning
The app can organize the question. You decide what it means.
Look at one event, compare similar events, or examine progress over time. Bolus Master keeps each level of review separate from prediction or treatment decision.
Single-event review
Describe what was recorded.
Review one meal, exercise session, or day using the available inputs, logged actions, notes, and later glucose data. It describes that event; it does not establish a pattern.
Repeated-pattern review
Compare similar events over time.
Place comparable recorded events side by side so recurring associations are easier to inspect. Repetition may support a useful question, but it does not prove why an outcome occurred.
AI-assisted organization
Organize—not decide.
AI can summarize possible associations, surface questions, and help prioritize records for review. The user remains responsible for interpreting the information and deciding what to discuss with a qualified clinician.
The AI boundary remains explicit.
AI-assisted summaries do not predict a glucose outcome, prove causation, prescribe treatment, or automatically change insulin settings. They organize recorded information for retrospective personal review.
Ready to challenge the workflow?
Bring the perspective one founder cannot supply alone.
If the visible decision trail makes sense—and you can see a place where your experience could make it stronger—request a place in the controlled peer-review cohort.
Read the safety and privacy boundary