03 / Predicting Demand, Load, and Risk

Machine learning & forecasting models

Models that predict what your business needs to plan around: demand, load, risk. Stated as a realistic range, not one number you're tempted to treat as certain.

Who this is for

Teams currently planning around a spreadsheet, a rule of thumb, or a model nobody trusts anymore, where a wrong forecast has a real cost, ordering too much stock, running out, or missing a risk.

What usually goes wrong

A single predicted number, with no sense of how uncertain it is, invites false confidence. We build models that give you a realistic range, and we set them up to update on a schedule automatically, instead of going stale the day they ship.

What you get
  • A forecasting model tested against your own past data to check it actually works
  • A likely range given alongside every prediction, not just one number
  • An automated pipeline that refreshes the forecast against live data on a schedule
  • A monitoring view that flags it when real-world results fall outside the model's expected range
What a forecast looks like

A likely range alongside the number, not one figure you're tempted to treat as certain.

Monthly demand index
Sample output · actual Jan–Aug, forecast to Dec
+17.5%forecast growth
5075100JanFebMarAprMayJunJulAugSepOctNovDecNOWJan, index 58 (actual)Feb, index 55 (actual)Mar, index 61 (actual)Apr, index 65 (actual)May, index 63 (actual)Jun, index 70 (actual)Jul, index 75 (actual)Aug, index 80 (latest actual)Dec, forecast median 94, range ±8
ActualForecast medianConfidence range
Stack
Python (statistical + gradient-boosted models)AWS Batch / LambdaPostgres / S3Grafana or equivalent for monitoring
Timeline

4–6 weeks · fixed scope

In practice
Manual → unattended
per-sensor calibration at commissioning, replacing hand-tuned thresholds, E-Rain Inc.
Contact

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