Demand and volume forecasting
What's coming next week and next season, by product, lane, or location — so staffing, stock, and capacity follow the math instead of the gut.
01What you get
Predictive Analytics & ML, concretely
Pricing signals
What this job, load, or order should cost based on what similar ones actually cost — surfaced at quoting time, when it changes the outcome.
Risk and churn scoring
Which customer is drifting, which invoice will go late, which job will overrun — flagged early enough to act, with the reasons attached.
Models that ship, not slides
The forecast lives inside your workflow — the dispatch board, the quote form, the dashboard — retrained on schedule and measured against reality every month.
02In practice
The same service, your industry’s shape
03Toolbox
Tools we reach for
Toolbox
- Python
- scikit-learn / XGBoost
- Time-series models
- PostgreSQL
- dbt
- Your ERP / TMS / CRM history
04How we work
Product speed, engineering quality
- 01
Learn the operation
We sit inside the process and watch how it runs today — the steps, the exceptions, the judgment calls, and every tool it passes through.
- 02
Show a working version
Not a mockup and not a deck. The first version you can actually use, in weeks rather than quarters.
- 03
Put it into real work
The system goes into your environment and connects to what you already run. It takes the routine path and hands the rest to a person.
- 04
Measure, then extend
We agree what better means before we start — time, errors, throughput — and report against it. The next process begins once the first one has earned it.
05Questions we hear
Fair questions
Do we have enough data for this?
Usually more than you think — a few years of orders or jobs is plenty for useful forecasts. We'll tell you honestly in the first week if the data can't support the question.
How is this different from AI hype?
This is the unglamorous math that's worked for decades, applied to your data and wired into your workflow. We commit to a measurable target before building anything.
What if the forecast is wrong?
Every forecast ships with its error range and a monthly accuracy report against reality. A model that stops earning its keep gets retrained or retired — you'll see it in the numbers either way.
Start with the process that costs you most
Tell us about the work your team dreads. We'll come back with what a working system would look like — and what it would take to prove it.