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Pricing

Priced in the open.

Every model in the library is included as an analysis, in every tier. Deployed models, priced on the page. No fee to connect your data, no custom quote to pry loose, no per-question meter running.

For a brand, you pay for the data you bring, not the number of brands. Each data set is one area of your business's records: sales and marketing is one; production and input costs is another; a risk team's own records is another.

Core
A$7,500US$5,000/ month
  • 1 data set
  • The full analysis library
  • Dashboard, intake & analyst chat
  • 5 seats included
Enterprise
CustomCustom
  • Every data set across your business
  • The full analysis library, on every data set
  • Custom seats & terms

The price per data set (or per client brand, for agencies) falls as you add more. Additional seats A$45US$30 / month each. Annual contract default, billed annually at ~15% below monthly. Every model included as an analysis, no meters. Enterprise combines the platform with the Prescriptive Suite, Continuous Experimentation on flagship brands, and deployed models per brand — a designed package, priced in the open like everything else.

Deployed models

Scores that stay fresh, priced like operations.

Every model in the library is included as an analysis. Ask how many of your customers are likely to churn, and the answer is part of your subscription. When you want the list itself — every customer scored, refreshed on your cadence and ready to act on — that is a deployed model, added to any tier. Certified, operator-released scores, ready to query alongside your own data.

Deployment · one-time, per brand
Initial deploymentModel fit + calibration on your data, operator certification, first released scores
A$6,000US$4,000
Freshness · recurring cadence
Monthly refresh default12 scheduled releases a year, plus on-demand headroom
A$2,500US$1,650/ mo
Weekly refresh campaign-active~52 scheduled releases a year, plus on-demand headroom
A$4,500US$3,000/ mo

Deployed-model pricing shown as a guide — finalised per brand at your deployment.

Experimentation

Tests, sold as a program — with the honest no built in.

Incrementality tests aren't priced per test — one test is not a practice. The program is per brand: design as many tests as you like; a test counts when you approve and freeze it, and nothing is charged until its readout is released. A design your data can't resolve is refused before a dollar moves — and a refusal is never billed. Every test is sealed when you freeze it, and every result carries the date it stops being true.

Program — Standardup to 4 tests a year, 1 in flight
A$3,500US$2,350/ mo per brand
Program — Continuous the always-on postureup to 12 a year, 2 concurrent
A$6,000US$4,000/ mo per brand
Additional test beyond the program cap
A$3,000US$2,000
Program setupone-time, per brand
A$4,500US$3,000

Design and feasibility reads carry no meter. A test draws on your program's allowance when you approve and freeze it, and nothing is charged until its readout is released.

Scenarios & prescription

Core evaluates. Enterprise prescribes.

Outcome Studio is included in every tier, on every analysis — what-if scenarios inside certified range, refusals rendered live, your assumptions always labeled as yours. The line between tiers isn't a feature paywall; it's a claim boundary: every tier can evaluate a scenario, and the enterprise module is where the platform starts prescribing.

Prescriptive Suiteorg-level module
A$9,000US$6,000/ mo

Multi-scenario stress-testing, full multi-parameter sweeps, exports — and the allocator: allocation ranges with stability statements, because within your book's certainty, some splits are honestly indistinguishable, and we'll tell you so rather than hand you false precision. Allocator: in build. Available on Agency Scale and above, or multi-brand direct clients.

About the fully loaded cost of one analyst — for the work of three functions.

A subscription sits at a data analyst's base salary. The fully loaded cost — super, overheads, recruitment, the management of the role — runs well past it. Add a deployed model and the maths still holds: one analyst, plus a fractional data scientist.