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Case study

experimental2026

RunwayOS

AI-powered financial runway predictor for SaaS founders. Tracks MRR, churn, and burn rate to forecast exactly when money runs out — with scenario modelling for different growth trajectories. Dashboard UI with metric cards and predictive charts.

Where wild ideas take flight and curiosity leads

Field notes

Role

Creator

Read order

Evidence first, then constraint → build → proof. Use the chapter rail to jump sections; orbit to adjacent work from the bottom of the case study.

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🔨IN PROGRESS

Evidence

System surface

No public screenshot on file—generative preview stands in for the visual layer.

Signature viewpreview

Story arc

How this shipped

Where wild ideas take flight and curiosity leadsThree beats: what pressed against the work, how the stack answered, and what changed once it was live.

I · Constraint

The brief

SaaS founders are often surprised by runway — MRR, churn, and burn are tracked in separate spreadsheets with no single predictive view.

II · Build

The craft

Unified dashboard pulling MRR, customer count, churn rate, and burn into a single AI-powered forecast engine. Scenario modelling lets founders stress-test growth vs burn assumptions.

III · Proof

What moved after launch

Replaces scattered spreadsheets with a single source of truth. AI forecasting surfaces runway risk weeks before it becomes critical. Scenario modelling enables confident fundraising conversations.

Inventory

Stack & signals

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© 2026 Elizabeth Stein

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