Turn History Into A Forward View You Can Plan Against
Mimasa AI reads the patterns already sitting in your governed data — seasonality, trend, and the events that broke it — and projects where the numbers are heading, with the assumptions written out in plain language.
Source Data
Every Projection
On Any Schedule
Why AI-Powered Forecasting Matters
Forecasting fails in most companies for a boring reason: the forecast lives in a spreadsheet that one person maintains, its assumptions are undocumented, and it is refreshed too rarely to be trusted when conditions change. The debate in the room becomes about whose spreadsheet is right rather than what the business should do.
Mimasa AI builds forecasts directly on top of your governed data, so the history behind a projection is the same history everyone else reports on. Seasonality, trend and known disruptions are detected automatically, and the assumptions behind each projection are written out in plain language rather than buried in formulas. When the underlying data refreshes, the forecast refreshes with it.
Because scenarios can be compared side by side, planning conversations move from a single contested number to a range with explicit drivers — what happens if collections slow by ten days, if a supplier slips a fortnight, if demand in one region holds while another falls. Teams commit to plans they can defend, and they see variance early enough to respond to it.
What You Get
The capabilities behind ai-powered forecasting in Mimasa AI.
Trend And Seasonality Detection
Mimasa AI separates the underlying trend from repeating seasonal movement so the projection is not thrown off by a normal cycle.
Plain-Language Assumptions
Each projection states what it assumed and which periods drove it, so planners can challenge it rather than trust it blindly.
Scenario Comparison
Adjust a driver — volume, price, headcount — and compare the resulting outlook side by side with the base case.
Refreshes With The Data
Projections re-run as new actuals land, so the outlook in front of you reflects the latest closed period.
How It Works
From connected data to a working outcome, in four steps.
Pick The Metric
Choose the governed dataset and the measure you want to project.
Set The Horizon
Choose how far ahead to project and which history to learn from.
Review Assumptions
Read what the projection assumed and adjust the drivers.
Share The Outlook
Publish the projection to a dashboard or a scheduled report.
Why It Matters
Plans Built On Evidence
Budgets and targets start from what the data shows rather than last year plus a percentage.
Earlier Warning
A drift in the trend surfaces while there is still time to act on it.
Shared Assumptions
Finance, sales and operations plan against one stated set of assumptions instead of three spreadsheets.
Where Teams Use It
Why Forecasts Need To Be Explainable, Not Just Accurate
A forecast that nobody can interrogate is a forecast nobody will act on with confidence, no matter how sound the underlying method is. Planning teams have been burned too many times by a number that arrived without context, only to be revised the following month for reasons nobody wrote down. Mimasa AI treats explainability as a first-class requirement rather than an afterthought: alongside every projection sits a plain-language account of the trend and seasonality detected, the periods that most influenced the outcome, and any known disruption the model adjusted for. That context is what lets a finance lead defend a number in a board meeting instead of simply presenting it.
Because forecasts are built directly on governed data rather than a maintained spreadsheet, they inherit the same lineage and refresh discipline as the rest of the platform. When a new month of actuals lands, the projection re-runs automatically, and the gap between the last forecast and the new actuals becomes a visible, trackable measure of how well the model is performing over time. That feedback loop is usually missing from spreadsheet-based forecasting, where the old projection is simply overwritten and the miss is never formally reviewed.
Scenario comparison is what turns a single number into a planning conversation. Adjusting one driver — a slower collection cycle, a delayed supplier shipment, a change in one region's demand — produces an alternative outlook that sits next to the base case rather than replacing it. Teams stop treating the forecast as a verdict and start treating it as a starting point for the specific what-if questions their business actually faces each planning cycle.
Common Questions
How much history do I need?
Enough to cover at least a couple of full cycles of whatever seasonality your business has — typically two years of monthly data.
Can I override a projection?
Yes. Drivers can be adjusted manually and the adjusted scenario is kept alongside the base case.
Does it explain itself?
Every projection is accompanied by the assumptions and the periods that most influenced it.