**Three layers, building on each other.** You already plan in story points. Smart Guess already measures which buckets your team delivers tight on — and helps you improve where you don't. Forecasting is the natural third layer — using the same data to answer a sharper question: **when will this ship?**

**The forecasting engine** runs two methods on your data: **per-item cycle times** when your buckets are tight (Path A), or **sprint-throughput sampling** when your flow is more stable (Path B). It picks the tighter one and tells you which it chose and why.

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Why Smart Guess

Same data. Sharper question.

By work type, cycle-time CV across our last 25 issues is **100%** — unpredictable. By story-point buckets (0.5pt, 1pt, 2pt), the same 25 drop to **26%** — highly predictable.

Sprint throughput across our last 11 sprints is also high (54–70% CV). Throughput-based forecasting alone would call us unforecastable.

The buckets are doing the work. They tell us which sizes we forecast tight on — and which oversized items to break down to tighten the rest.

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Proof from our own team

The buckets are doing the work.


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The team gave a date in good faith. Then sprint after sprint of slippage made the roadmap meaningless.

Leadership stopped trusting the timeline. You stopped sharing one.

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What it actually feels like

You've been burned before.

Select issues from your backlog — an epic, a release, or any JQL query — and Smart Guess forecasts the finish date from your team's actual delivery history.

Set the scope. Find out when it ships.

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**1. Set the scope.** Use any JQL query, an epic, a sprint, a release — whatever scope you care about.

**2. Don't worry about unestimated work.** Smart Guess pulls your team's historical cycle times and runs a Monte Carlo simulation — each unestimated item is modeled as a draw from your team's actual delivery pattern. Forecast still works, just wider until you size more items.

**3. See the forecast.** Likely, plan-for, and worst-case finish dates, with the confidence math baked in.

**4. Track and steer.** Smart Guess compares actual progress to the forecast as the team ships. Anything that pulls the team off plan — scope added, production issues, time spent on other work — shows up as drift, not unexplained slippage. You see exactly what's pushing the date out, early enough to steer.

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How it works

Four steps to a forecast that holds up.

The credibility gap widens with every missed date — and so does the gap between what your team can actually deliver and what stakeholders think they should.

A date you can defend isn't about being right every time. It's about showing the math, so the conversation moves from "is the team underdelivering?" to "how should we adjust?"

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Cost of standing still

Every date you give is a guess.

**Understand your predictability — and where to improve it.** "Snapshot: 1pt and 2pt buckets at 26% CV — tight. 5pt bucket at 78% — wide. Most of our variance is concentrated there. Refining these items into smaller ones will tighten our forecast."

**Forecast — and see where the weak spots are.** "Selected 18 stories for the next epic. Forecast: 18–28 working days at 85%. Noesis: '5 unestimated items plus 3 items in your wide 5pt bucket are driving most of the width. Tighten either to narrow the range.'"

**Track what's pulling the forecast off.** "Baseline saved. Week 3: forecast widening by +5 days. Noesis: 'Cause: 4 production incidents consumed 2 days of capacity, plus 2 items added to scope.' We see exactly what's pushing the date — and have the data to push back."

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What changes

From hedged dates to forecasts you can defend.

**The selection** — your chosen issues with cycle-time history per bucket, so you see at a glance which items are widening the forecast.
 
**Noesis sidebar** — your AI coach: picks the forecasting method your data supports, names what could break it, flags which buckets are widening the forecast, and catches whatever's pulling the date as the team ships.

**The forecast** — likely / plan-for / worst-case dates, grounded in your team's history. Save it as the baseline to track and steer against.