( 05 )Forecasting

Predictive analytics and demand forecasting you can plan on.

We build predictive analytics that turns your history into forecasts you can act on. Demand, risk and pricing models update as new orders come in, and every forecast shows how confident it is.

Start with a Spark session

See next quarter before it arrives.

( Drag )
Stock is ordered on gut feel and last year's spreadsheet.
( Go )
Forecasts that update as orders come in, with the confidence shown.

( Sound familiar )

Signs it's time.

If two or more of these sound like your week, it's worth a sprint.
  1. 01

    Purchasing is planned from last year's spreadsheet and a gut feel.

  2. 02

    You either run out of the products that sell or sit on stock that doesn't.

  3. 03

    Planning meetings argue about which number to trust.

  4. 04

    Nobody can say quickly what happens to the plan if demand drops by a tenth.

( What we build )

What Predictive Insights covers.

Each piece can ship on its own. We start with the one that removes the most drag.

  • Demand forecasting

    Forecasts by product, location and week that learn from seasonality, promotions and recent orders.

  • Risk scoring

    Scores for late payments, supplier delays or failing equipment, so attention goes where the risk is.

  • Scenario planning

    What-if models that show how the plan changes when demand, prices or lead times move.

  • Inventory optimization

    Reorder points and quantities based on the forecast and its uncertainty, not a fixed rule of thumb.

  • Pricing models

    Models that estimate how price changes affect volume and margin before you make them.

( Where it fits )

Built for real teams.

Retail
Forecast demand by store and week to cut stockouts and overstock.
Manufacturing
Plan materials and capacity from a forecast that updates weekly.
Technology
Project revenue and renewal risk from usage and pipeline data.

( How it runs )

Spark. Build. Go.

Every predictive insights project runs the same three moves.
  1. Step ( 01 )

    Spark

    One working session with your team to find where the hours leak. Then we pick the single idea worth building first.

  2. Step ( 02 )

    Build

    We design and engineer a working AI product, wired into the tools you already use, and put it in front of real users early.

  3. Step ( 03 )

    Go

    We ship it, measure it and tune it until it runs on its own. Then we find the next drag and go again.

( Questions )

Fair questions.

( 01 )

What is predictive analytics?

Predictive analytics uses historical data and statistical or machine-learning models to estimate what is likely to happen next, such as next month's demand or which accounts are at risk.

( 02 )

How much history do we need?

It depends on the question. Weekly demand forecasting usually benefits from at least a year or two of history so seasonality shows up, and we check what your data supports before committing to a model.

( 03 )

Why show confidence instead of one number?

Because a forecast is a range. Showing it lets planners decide how much buffer to hold, instead of trusting a single figure that will rarely be exact.

( Keep going )

Other AI solutions.

Let's go.

Tell us where the hours go today. We'll come back with the first thing worth shipping, and what it takes to get it live.

[email protected]