KNOWW

Workbench / Use cases

One table in, a validated prediction out, in every field

Industries

One model. Every field's question about tomorrow.

Each of these questions is one table with one outcome column. The Workbench runs the same models and the same checks on every one of them. Some questions below have a seeded run you can open on day one; the rest are the same mechanics on a table like yours.

Financial services

Starting here
Questions
  • Which clients will miss next month's payment?
  • Which small-business loans default within six months?
  • Which accounts roll into collections?
What to bring
Balances & utilisationPayment historyTransactionsLinked accounts
Why first

The richest histories and the strictest model review. Two of the seeded runs are a card book and a small-business loan book. If a run passes here, it passes anywhere.

Insurance

Questions
  • Is this claim genuine?
  • Will this policyholder renew?
What to bring
Claims historyPolicy changesLinked policies
You get

A probability per claim or policy, the drivers behind it, and a flag on rows whose range is too wide to act on.

Supply chain

Questions
  • Will this shipment arrive late?
  • Which supplier is about to slip?
What to bring
Lead timesDelivery historySupplier → lane → warehouse links
You get

Late-arrival probabilities per shipment, still current when routes and suppliers change.

Retail & inventory

Questions
  • Which customers will take up an offer if we call them?
  • Will this customer stop buying?
  • How much sells next week?
What to bring
Sales historyOrdersStock levels
You get

A probability per customer with the drivers behind it, a threshold view for the call list, and forecasts where history is short. The seeded cross-sell run uses a public bank-marketing dataset.

Manufacturing & maintenance

Questions
  • Which machine will need maintenance next?
  • Will this machine fail within 30 days?
  • What will maintenance cost next month?
What to bring
Operating hoursSensor readingsFault & repair historyMachine → component links
You get

Failure-risk probabilities and maintenance-cost forecasts, with an 80% range for each machine and the facility total, to plan servicing and budgets.

See the maintenance run

Energy & operations

Questions
  • How much will the plant use in each hour of tomorrow?
  • Which batches will fail quality checks?
What to bring
Hourly or daily readingsCalendar & shift patternProcess measurements
You get

An hour-by-hour forecast for the next day with a range, scored against a same-time-last-period baseline. The seeded run uses a public steel-plant dataset.

Is your problem a fit?

Three questions to check.

If you answer yes to all three, the Workbench can run your table, whatever your industry.

  1. Do you keep records over time?

    Rows for things (customers, assets, orders) with a history of what happened to them.

  2. Is there a yes/no or a number you want to know ahead of time?

    Will it default, churn, arrive late, return, escalate? How much will sell?

  3. Does the answer matter enough to act on?

    A decision, a budget, a staffing plan or a customer depends on it, so you want to know how sure the prediction is.

Tell us your question
Validation & trust

Evidence your reviewers already ask for.

Whether the reviewer is a bank's model-risk team or a COO, a run shows its work.

Validated like any model you run

  • Out-of-time backtests on your own history
  • A leakage test: rows after the cut-off are scrambled and no feature may change
  • Side-by-side results against what you use today
  • Calibration: do 30% predictions happen 30% of the time?

Every prediction has drivers

  • The strongest drivers per row, signed
  • Ready for an analyst, an auditor or a customer letter

Shows when it isn't sure

  • An 80% range on every number
  • Calibration curves on every probability
  • Rows with a range too wide to act on are flagged for a person

Your data stays with you

  • Runs on our server; no outside AI service sees a row
  • Kept for 30 days, deleted the moment you ask
  • Private deployment inside your environment for pilots
How every pilot runs
  1. Shadow mode

    The model predicts on live cases, in our environment or yours. Nothing it says changes a decision yet.

  2. Side by side

    The model and your current approach are compared on the same cases: accuracy, calibration, stability over time.

  3. Sign-off

    Your reviewers check the backtests, calibration and drivers, as for any model.

  4. LIVE

    The model predicts in production. Rows flagged for review go to your current process.

Questions

What teams ask first.

Short answers to the questions that come up before the first upload. The guide inside the Workbench covers every screen.

Something else?hello@knoww.ai

One table, one row per thing, the outcome as a column, blank where it is not known yet. An ID for each row, and a date if you have one. CSV or Excel, up to 50,000 rows and 500 columns in the free month.

Request your free month

What do you want to predict?

Five runs, 50,000 rows each, free for a month. Tell us the question and what you use today. We review every request by hand and email you when your workspace is ready. If you already know you want a pilot next to your current model, say so below.

hello@knoww.ai
Request your free month
We send a confirmation link here.
The question you'd like answered ahead of time.
It goes on the leaderboard next to the foundation models.

We review every request by hand and email you when your workspace is ready. Please don't include sensitive data.