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Estimator overview

Estimator lets you build parameterized probabilistic models — anything from a consulting cost estimate to a hiring plan, TAM/SAM/SOM sizing, product NPV, or marketing spend break-even analysis. You write a model once and reuse it across many concrete cases, each stored with its input values and a snapshot of results.

What it does​

You define a model using either:

  • Formula engine — a mini-language expression where you declare random variables and derived calculations, then run 1,000 simulation iterations to see the output distribution.
  • Spreadsheet engine (Pro) — upload an .xlsx workbook, mark assumption and output cells, and run the same simulation against your real spreadsheet.

A sketch (no user inputs) works like a one-off calculator — write a model and read off the results. A template (one or more user input fields) is reusable: a runner fills in the fields and saves each case as a named run. All runs for a template are stored together with the inputs and output metrics for comparison and export.

Who this is for​

  • Analysts and consultants building repeatable cost or revenue models they hand off to clients or colleagues to fill in.
  • Finance and ops teams who want to capture uncertainty in a forecast rather than pretending a single number is right.
  • Product managers sizing TAM, pricing scenarios, or feature adoption ranges.
  • Engineers estimating latency budgets, failure rates, or capacity numbers with known uncertainty.

Layout​

The tool has four screens:

ScreenPathPurpose
Author view/app/estimator/<id>/Build and edit the model (formula textarea + derived cards, or spreadsheet grid)
Template detail/app/estimator/<id>/runsView and compare all saved runs for a template
Run view/app/estimator/<id>/runs/<run_id>/Fill in a run's inputs and see results
Draft run/app/estimator/<id>/runs/newCreate a new unsaved run against a template

For templates, the author view and run view are linked by a Template / Runner toggle in the toolbar.

Next steps​

  • Core concepts — sketches, templates, runs, engines, and the variable model
  • How-to guides — task-oriented walkthroughs
  • Reference — mini-language syntax, distributions, metric types, and API endpoints