Funding clarity, from idea to award.
Compare funding opportunities, allocate proposal capacity and model uncertain outcomes using transparent, synthetic assumptions.
Funding pipeline outlook
Illustrative cumulative award outcomes from reproducible simulations
Portfolio decision brief
Computed from your current hypothetical pipeline
Most promising opportunities
Highest expected funding under current assumptions
Coverage by research area
Expected awarded funding, grouped by category
Stress-test your pipeline.
Explore competition, proposal quality and submission capacity without implying guaranteed funding.
Scenario assumptions
Choose a scenario or customize the controls to explore a different operating environment
Outcome probability distribution
Simulated total funding across 600 portfolio outcomes
Capacity-aware application shortlist
Optimal subset by expected funding under the hours budget
Interpretation
What the model can and cannot establish
Build a stronger pipeline.
Track simulated funders, proposal readiness, requests and evidence-based priorities, all stored locally.
Opportunity library
Editing an opportunity updates all charts, deadlines, capacity choices and scenarios
| Opportunity | Deadline | Request | Estimated chance | Readiness | Hours | Status | Actions |
|---|
Add opportunity
Use synthetic values only. Maximum 25 records including archived entries.
Stay ahead of the calendar.
Browse application deadlines by month and see which proposals need attention first.
Upcoming deadlines
All active draft and submitted opportunities
Next six months
Count of active opportunities closing by month
What the numbers mean.
Inspect the assumptions behind rankings, capacity allocation and uncertain outcomes.
Model design
For a draft opportunity, requested funding multiplied by its subjective probability of award after a scenario multiplier, capped at 95%. Submitted records participate in the funding forecast but do not consume future preparation hours.
Readiness is a descriptive indicator, not multiplied into the award probability. Avoids counting proposal quality twice without calibrated evidence.
Exact dynamic programming chooses a subset of draft proposals with total preparation hours within the budget, maximizing sum of expected award dollars. Drafts past their deadline are excluded.
600 deterministic, seeded portfolio realizations simulate independent 0/1 wins for submitted proposals and capacity-selected drafts. Funding is assumed paid in full upon award.
Previously awarded proposals appear in the registry but are excluded from uncertain future award totals. Archived opportunities do not participate in model outputs.
Scope, limitations and safety
Percentages are user-specified assumptions, not estimated from historical funding outcomes. Simulated confidence bands describe this model, not real-world predictive certainty.
Deadlines depend on the device date and are not checked against any institution. Funding constraints, co-financing, indirect costs, institutional eligibility and ethical review are not modeled.
Grants may be correlated, budgets partially paid, or mutually exclusive. Those effects are deliberately omitted. Multiple funding sources do not imply additive unrestricted revenue.
Data is stored in this browser via localStorage when available. CSV and JSON are generated locally. There are no external fonts, trackers, APIs, accounts or network calls.