Don’t take my word for it. Drive it yourself.
Six of the models behind my case studies, stripped back and made interactive. Move the inputs and watch the numbers move — the arithmetic is written out beside every result, so you can check my working rather than trust a headline.
Every demo runs entirely in your browser on synthetic data. Nothing you type is sent anywhere.
What is manual reporting actually costing you?
The first thing I work out on any automation engagement. Put your own numbers in — the formula is shown underneath, so nothing is hidden in a black box.
per week
salary plus overhead
be conservative — I would rather under-promise
- Time spent today
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- Cost of that time
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- Saving per month
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- In working days
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The arithmetic
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This is the model behind the automation & BI practice, where a −80% cut in manual effort came out of exactly this sum.
One dashboard, eight questions.
How a real executive dashboard behaves: one chart, a metric switcher, and a written reading of what the series actually says. Hover or tab through any point for its value.
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The same pattern as the logistics control tower — one screen a leadership team can actually run a meeting from.
Ask the warehouse a question.
A sandbox with four tables — orders, customer_monthly,
inventory_health and campaigns. Run one of the examples, or
write your own SELECT and see what comes back.
Queries run against an in-browser fixture — there is no database behind this page, and nothing you type leaves your machine.
Which account is about to leave?
A weighted scorecard, not a neural network — which is the point. Four signals, visible weights, and a verdict a customer-success lead can act on the same morning.
Signal weights: —
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— risk score
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Read the full build in the churn early-warning case study.
How far ahead can you honestly see?
Push the horizon out and watch the confidence band widen. This is the conversation worth having before anyone commits to a number twelve months out.
- Point forecast
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- 80% interval
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- Uncertainty
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- Implied growth
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Method —
The production version of this sits in the retail demand-forecast case study.
Watch a nightly run, end to end.
Five stages, safe to re-run, with rejected rows quarantined rather than quietly dropped. Start a run and each stage reports in as it finishes.
- Extract Warehouse read replica, from the last watermark.
- Validate Schema contract checked; bad rows quarantined.
- Transform Currency, labels and grain normalised.
- Load Idempotent upsert into the reporting mart.
- Publish Dashboards refreshed, run logged, team notified.
- Rows read
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- Rows loaded
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- Quarantined
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- Tables
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- Run time
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The same five stages run behind every pipeline in the case studies.
Want one of these running on your own data?
These are toys with the real logic inside them. The production versions read from your warehouse, refresh on a schedule and get monitored — tell me which number you are trying to move and I will tell you what it takes.