Planning Poker that turns estimates into predictable delivery.
Planning Poker Without Predictability Breaks Trust
Your team estimates every sprint. But do those numbers connect to reality?
- Same estimates, different outcomes → Your 5-point stories vary wildly, but you can't see which ones or why
- Credibility erosion → Stakeholders lose trust sprint by sprint, and you have no data to explain
- Wasted history → Years of estimation data sitting in Jira, teaching you nothing

We've Been There
Estimation isn't the problem. The disconnect between estimates and delivery reality is.
Smart Guess was built to bridge that gap — turning your existing Jira data into predictive insight.
No consultants. No complex setup. Just clarity on what's actually predictable and what isn't.
What's Your Code Review Costing You?
Enter your numbers. See where you stand. Learn more about Time In Status for Jira
Your one team
With reviews complete in 3 days
The Hidden Cost of Staying Busy
Adjust levers to see WIP impact
vs. Competitor one team
With reviews complete in 0.5 days
They just don't wait 3 days for reviews.
Every. Single. Year.
What Staying Busy Actually Means
2-week sprint, 3-day reviews
Every review return means dropping what they're doing.
With Smart Guess, teams deliver with confidence
What Success Looks Like
- Consistent delivery → deadlines hit, trust restored
- Healthier teams → less stress, stronger morale
- Smarter decisions → insights drive lasting improvements
- Competitive edge → flow optimization keeps you ahead
Estimates that finally mean something
See which ones hold up. Improve the ones that don't.
Start free for 30 days.
- Historical data → Forecast probabilities
- Recurring blind spots → Visible patterns
- Gut feeling → Evidence-based decisions

Explore More Ways to Improve Delivery
Async Planning Poker
Not everyone can join the meeting. Async Planning Poker lets teams estimate across time zones — to speed up refinement.
Flow Intelligence
Understand how work actually flows through your system. Spot variability, bottlenecks, and unpredictable work — using real delivery data.

