FlowProof helps delivery leaders in software and product make better decisions — what to commit to, what scope to cut, when to add capacity, and which risks to raise early. It turns your work-item CSV into validated, probability-ranged forecasts using seven trust checks, Little's Law, and Monte Carlo simulation. Forecasting is the capability; defensible decisions are the outcome. Not a creator, content, or productivity workflow tool.
Show leadership an honest ship date for your team's backlog — with the confidence interval, not a single-date promise.
Every FlowProof output maps to a decision a delivery leader actually has to make — and gives you the evidence to defend it.
Decision
Commit or push back
Can we hit this date?
See the 50/85/95% ship dates before you sign up for one.
Decision
Cut scope with confidence
What fits in the window?
Know how many items you'll realistically ship — and which to defer.
Decision
Staff or unblock early
Where is flow breaking down?
Aging + WIP signals surface the risks worth escalating this week.
Decision
Defend the forecast
Would this hold up in the steering committee?
Seven trust checks + evidence rows so no one can wave the numbers away.
These numbers come from the same Monte Carlo engine FlowProof runs in-app — 4,000 trials over a 56-day sample of daily throughput (mild weekend dips, occasional batch days). Change the backlog or the horizon and the percentiles recompute instantly.
Used by the 'When will it ship?' card · range 5–200
Used by the 'How many will land?' card · range 3–60
Backlog of 40 items · throughput sampled from the last 56 days
Read it like a weather forecast: "We'll likely finish by Jun 29, with a 15% chance of slipping past it." No single-date theatre.
Horizon of 14 days · same throughput sample, same 4,000 trials
The 95th-percentile count is the floor: commit to this number publicly, surprise stakeholders to the upside with the 50% line internally.
Sample data, seeded for a stable preview. Your real numbers will differ — and FlowProof tells you the trust score for them before showing the forecast.
A product team has 40 backlog items to ship before month-end. They upload 56 days of throughput history (2–6 items per active day, weekends off). FlowProof validates the data, then runs 10,000 Monte Carlo trials. Here is the output.
Scenario inputs
Backlog: 40 items · Horizon: 28 days · History: 56 days of daily throughput
Based on the 56-day throughput sample · 4,000 trials
Same throughput sample · 4,000 trials
The decision this unlocks
The 85% date for 40 items is Jun 29 — inside the 28-day window. The delivery lead can tell the steering committee: "We are 85% confident of finishing 40 items by month-end." If they ask for 60 items, the forecast shows that would push the 85% date to Jul 2 — so they must either cut scope or extend the deadline.
In 28 days the team is 95% likely to complete at least 75 items. That is the public-safe floor. Internally, plan around the 50% line (75 items) and treat anything above it as upside. No single-date theatre, no surprise slip.
FlowProof does not just give you numbers. It gives you the evidence to make a call. Step through each output and see what decision it unlocks.
Check trust first
Before any forecast runs, FlowProof scores your data.
Sample size · History window · Recency · Idle days · Outliers · Date integrity · Conservation of flow
Decision this unlocks
If the trust score is high, the forecast is built on clean data. You can paste the percentile range into Slack and defend it in the steering committee.
Action: If trust is low, fix the export first — a broken forecast is worse than no forecast.
A delivery lead switched from single-date promises to probability-ranged forecasts. Stakeholders stopped asking for status updates because the plan was already honest.
A product team used the 'How many will land?' forecast to cut scope before kickoff instead of discovering the gap six weeks in.
An engineering manager spotted aging WIP via flow signals and reallocated capacity before the steering committee — not after it.
Typical outcomes based on anonymised usage patterns. Your results will depend on data quality, cadence, and team context.
Paste or upload a CSV — no Jira integration required. Map your columns, we do the rest.
Seven checks against your data. Sample size, conservation of flow, Little’s Law, date integrity, stale items, and more.
10,000-trial Monte Carlo with percentile dates. If trust is low, the forecast button greys out — by design.
Before any Monte Carlo trial runs, FlowProof inspects your CSV against seven independent checks. Each one answers a question you'd ask if you were grading the data yourself.
Are there enough finished items to draw a stable distribution from? Fewer than ~20 and the bands get wide fast.
Does the data cover enough recent calendar time? A 2-week window will not represent a quarterly pattern.
Are the most recent weeks actually present, or did the export quietly cut off a month ago?
How much of the window is zero-throughput? Long idle stretches usually mean missing data, not real pauses.
Did one day batch-close 30 items? Those spikes inflate the average and lie about your real cadence.
Are started/finished dates present, parseable, and in the right order? Missing or inverted dates corrupt cycle time.
Do items that started actually finish, or is work disappearing? Mismatched arrivals and departures break Little's Law.
If two or more checks fail at error severity, the Run forecast button is disabled until the data is fixed. Warnings still let you forecast, but they show up on the result so nobody mistakes a noisy run for a confident one.
The point isn't to gatekeep — it's to make sure you never paste a "85% by Aug 12" date into Slack that was built on three weeks of broken exports.
One delivery system free, forever. Add Starter for unlimited forecasts, Small Team for multi-workspace collaboration, or Enterprise for SSO and invoicing.
Flowgate — the guided setup that validates data and builds your option funnel — is included on every plan. It's a capability inside FlowProof, not a separate product or add-on.