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DELIVERY RISK CALCULATOR

See beyond the average. Model your delivery timeline, then see the same scope modeled for an expert AI delivery team.

Traditional estimates produce a single number, the P50. Half the time you'll miss it. This tool runs a Monte Carlo simulation across thousands of scenarios to show the full risk tail for your team as it runs today. Then it runs the same scope through a second model, a small senior pod working with AI, and shows the timeline, the variability, the people, and how much more ships in the same calendar time. Every assumption is yours to change.

How do you estimate today? Both convert to calendar weeks
Engineers, QA, BA, PM, whoever is on this work. Used for people and cost comparisons.
What does your estimate buy? Pick the rung your number describes
POC
Prove it works
MVI
Minimum viable increment
MVP
Minimum viable product
Cadillac
Polished and complete
Ferrari
Advanced and tuned

What kind of work is it?
Greenfield
New product or service
Feature on existing
Extending what you run today
Modernization
Replatform, rewrite, migrate
Integration
Connecting systems and vendors
How does your team run?
Waterfall
Phase gates
Scrum
Sprints
Kanban
Continuous flow

Estimation maturity Sets how wide the uncertainty is
Early Discovery
Concept / exploration
Preliminary / Rough Cut
Initial scoping done
Well Refined / Stable
Detailed breakdown
Highly Mature / Locked
Committed plan

chevron_right Advanced: every assumption in the model

Nothing here is hidden. Each choice above sets sensible defaults for the numbers below; change any of them and the simulation uses your value. The AI team values are our modeling assumptions, not measured results, and they are marked. All of it travels in the share link.

Your team today
0.30
How spread out your scope uncertainty is relative to the estimate. 0 is perfect certainty; 1.0 means the standard deviation equals the mean. The maturity stage sets the range.
Multiplies the CoV. Waterfall widens the tail; Kanban trims it a little.
Calendar time added no matter what: gates, sign-offs, release trains.
Applies to both lanes. Migrations and integrations carry more unknowns than greenfield.
Converts person-months to weeks and weeks back to months.
Salary, benefits, tooling, management. Adds a cost row to the comparison.
Value ladder

Effort at each rung relative to the MVP. The tool scales your estimate up and down the ladder with these, so the ratios matter more than the absolute values.

Expert AI delivery team Modeling assumptions

A small senior pod working with AI, carrying the full lifecycle. These defaults describe how we model that pod. They are not a measured multiplier, and you should replace them with anything you know better.

0.30 means the pod spends 30% of the person-weeks your plan does. Set by the kind of work.
Engineer, architect, delivery lead. Small and senior by design.
Fixed time spent understanding the work before building, which is what earns the lower variability.
The pod's lane runs at .
Adds a cost row. Capacity tiers are on the subscription page.
Things that can be built in parallel. Used to suggest a capacity tier.
0.30
Defaults to the uplifted maturity stage. Fewer handoffs and continuous delivery keep the tail short.
Simulation
1,000 to 100,000. More runs, smoother curves.
Resolution of the chart.
Any seed reproduces the exact same picture. The share link carries it.