Predictive Intelligence & Forecasting
The record is set.
What comes next is printed as a range.
We turn your own history into an almanac of what is coming (demand, revenue, churn, inventory) where every entry carries a band, the drivers behind it, and a place in the tool your team already opens.
The almanac
Demand · units / week
The band widens the further out the page looks. That's the forecast being honest, and it says nothing bad about the model.
A range
Printed on every entry
Your history
Not an industry average
Nightly
The page resets itself
Held-out
Accuracy proven first
The brief
Your history already keeps an almanac. Nobody has printed it.
Most planning still runs on last month's totals and a confident voice in the room. Meanwhile the patterns that decide next month are already sitting in your own record: the season, the trend, the promotion, the signal that always turns first.
Forecasting reads that record and sets it in type: what is coming, how wide the uncertainty around it really is, and what is pushing it. It's a page to plan against, and nobody is asked to simply believe it.
We don't hand you a model in a notebook. We put the page where the decision gets made.
What the page is for
Buy to the demand instead of the memory
Reorder points come off the forecast instead of last month’s totals, so the fast movers stay on the shelf and capital stops sitting in the slow ones.
Commit to a number you can defend
Revenue and cash land inside a stated band, so the plan you take into the room has the confidence behind it written down.
Reach the customer while it still matters
Churn shows up as a drift in the signal weeks before the cancellation, which is the only window where a conversation still changes the outcome.
The entries
The numbers you plan the week around, each with its own page
Demand
± 4%p. 04What sells, by product and location, far enough ahead to buy and staff for it.
Revenue & cash
± 6%p. 11Where the month and the quarter land, with the spread around the landing.
Churn & retention
± 3%p. 19Which accounts are drifting, scored early enough that outreach still lands.
Inventory
± 5%p. 26Reorder points and cover levels that miss both the stockout and the dead stock.
Capacity & staffing
± 7%p. 33How heavy the week runs, so the schedule is built against the load instead of a hunch.
Leading signals
± 9%p. 40The indicators that turn before the totals do, flagged while the turn is still cheap.
The season
What the calendar looks like from your side
Weeks 1–2
Reading the record
We pull the history together (sales, seasonality, promotions, the events that moved the number) and tell you plainly what it can and can't support. Some questions your data simply can't answer yet, and you hear that here instead of after the build.
Weeks 3–5
Setting the type
Models are fit and validated against held-out history, chosen on measured accuracy over novelty. You see the error on periods the model never saw before anything ships.
Weeks 6–8
Printing the page
The forecast gets its band, its drivers, and its home: the dashboard, sheet, or alert your team already opens. Nothing lives in a notebook only one person can run.
Ongoing
Reprinting it
The pipeline refreshes as new data lands and we score every past call against what actually happened, so accuracy is a number you can watch instead of a promise.
How a page gets set
From a raw record to a printed entry
Gather
Every source that moves the number, unified into one history the model can read.
Fit
Candidate models trained and scored against held-out periods. The best measured one wins.
Band
The prediction gets its range and its drivers before it gets a place to live.
Bind
Delivered into the tools your team already checks, with the action spelled out.
Why the band is the point
A single number tells you what to hope for
A range tells you what to prepare for. Print one number and the risk doesn't disappear. It just stops being visible, and it shows up later as the month nobody costed.
So every entry carries its spread. You stock, staff, and spend against the likely call, and you hold cover for the edges, with your eyes open on both.
Next quarter · revenue
Plan cover for it. Do not budget on it.
Where the weight of the evidence sits.
Have the stock and the staff to catch it.
One line on the page, three ways to be ready for it.
What changes once it is printed
The quiet things a range fixes
The argument ends
Two teams stop bringing two spreadsheets to the same meeting. There is one number, one range, and one place it came from.
The drivers are visible
Every forecast says what is pushing it (the promo, the season, the trend) so your team can sanity-check it against what they already know.
Being wrong gets cheap
A stated band means the downside was already planned for. The bad month becomes a scenario you costed instead of a surprise you absorb.
Trust is earned in public
Past calls are scored against actuals where everyone can see them. The model earns its authority instead of being handed it.
Selected work
Pages we have set before
Bound into the book
What you are handed at the end
Trained, validated, explainable, and delivered where you already work, with the accuracy tracked in the open so trust is earned instead of assumed.
- 01Forecasting models trained and validated on your own history
- 02A confidence band on every entry, never a bare point estimate
- 03The drivers behind each call, surfaced in plain language
- 04Dashboards and alerts wired into the tools you already open
- 05A refresh pipeline that reprints the page as data lands
- 06Accuracy tracked against actuals, visible to everyone who reads it
The editions
Every trade keeps a different one
The retail edition
Store-level demand and reorder cover that survives a promotion calendar and a peak season.
The hospitality edition
Occupancy, covers, and booking pace forecast far enough ahead to move rates and staff schedules.
The subscription edition
Churn and expansion scored per account, so success teams spend the week where it counts.
The distribution edition
Volume and capacity forecasts that smooth routing, replenishment, and the labor plan.
First conversation
Bring your history. We'll tell you what it can honestly predict.
Including the parts it can't, before anyone signs anything.
Choosing Flaidex for this
Four things we won't do differently
We say what your data can't do
If two years of history won't carry the forecast you want, you hear it in the first conversation instead of in the third month.
Accuracy is measured before it is claimed
Every model is scored on periods it never saw. You read the real error before you decide to rely on it.
Nothing ships as a black box
The drivers come out with the number. A forecast your team can't interrogate is one they'll quietly stop using.
It lands where the decision is made
The model is only half the job. The work is finished when the forecast sits in the tool where somebody acts on it.
Working with us
What the engagement itself is like
A small team that stays
The people who read your history are the people who build the models and the people who hand them over. Nothing gets re-explained to a new bench.
Plain reporting
You get the accuracy number, including the weeks it slipped. We'd rather show you a miss than manage the story around it.
Built to be inherited
The pipeline, the retraining cadence, and the scoring are documented so your team can run and tune it without us on a retainer.
Questions
What people ask before the first page
Q1How much history do we need?
Two years usually gives strong seasonal forecasts, but plenty of useful work starts with less and sharpens as data accrues. In the first conversation we tell you honestly what your record can and can't support, including when it can't yet support the thing you asked for.
Q2Why a range instead of one number?
Because the future isn't a single number, and printing one hides the risk instead of removing it. A range lets you plan against the likely case and hold cover for the edges. You get the cold edge, the likely call, and the warm edge, each with the confidence behind it.
Q3Is this a black box?
No. Every forecast surfaces its drivers, what is pushing the number up or down, so your team can check it against what they already know about the business. A forecast nobody can interrogate is one nobody keeps using.
Q4How accurate will it be?
It depends on your data and on how predictable the thing genuinely is. We validate against held-out history so you see real measured error before you rely on it, and we keep scoring calls against actuals after launch so the number stays honest.
Q5Where do the forecasts show up?
Wherever your team already works: a BI dashboard, a spreadsheet, or an alert in the inbox or chat, with the recommended action attached instead of a chart to interpret.
Q6Does it keep improving?
Yes. The pipeline refreshes as new data arrives and we retune on a cadence, so the models track the business as it changes instead of slowly drifting away from it.
Elsewhere in AI Integration & Development
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