Predictive Systems
What if the system told you three days before the failure? ML models built against your operational data, not generic benchmarks, so you're acting on your actual patterns.
Predictive systems are only useful when they're calibrated to your data and your context. A model trained on industry benchmarks will tell you things that are roughly true for companies like yours. A model trained on your operational history tells you what's actually happening in your business, ahead of time.
We build and deploy ML models for real operational problems: maintenance schedules that adjust to actual wear rather than manufacturer estimates, demand signals that reflect your specific customers rather than category averages, anomaly detection tuned to your normal rather than a generic baseline. The model is the starting point; keeping it accurate over time is the work.
- Problem framing: making sure what you're predicting is actually predictable from the data you have
- Data audit and preparation — the part most projects underestimate
- Model development, validation and testing against held-out data before it goes near production
- Production deployment with monitoring for drift and degradation over time
- Plain-English reporting on what the model is doing and where its confidence is lower
- Ongoing maintenance and retraining as your data changes
Quick questions
Do we have enough data to build something useful?
That's always the first question. Sometimes yes, sometimes not yet. We'll look at what you have, tell you honestly whether it's enough, and if it isn't, tell you what would need to be collected and for how long. There's no point starting a prediction project on data that can't support it.
How is this different from the analytics we already run?
Analytics tells you what happened. Predictive models tell you what's likely to happen next, and often why. The data can be the same; the question you're asking is different. The value is in acting before the event rather than reporting on it after.
What happens when the predictions are wrong?
They will be sometimes. Every model has a false-positive and false-negative rate, and we're honest about it up front, build monitoring to track it, and tune accordingly. A model that catches most of the problems you'd otherwise miss entirely is still well ahead of finding out the hard way.
We've shipped systems still running a decade later.
Custom software, AI agents and automation for real business problems. We design, build and run production systems — not prototypes. We ship working software that scales from a first version to national platforms. All of Build →
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