CLIEncoders · AI & Agents
Predictive analytics and forecasting
A forecast is only useful if someone changes a decision because of it. We start from the decision and work backwards, which frequently produces a simpler model than expected.
Start from the decision, not the data
The question is never really 'can you predict this'. It is 'what would we do differently if we knew, and how much warning do we need'. Predicting a bearing failure two hours ahead is useless if the maintenance window is a week away.
That framing sets everything: required lead time, tolerable false-alarm rate, and how much accuracy is actually worth paying for. It also occasionally reveals that a threshold alert on an existing sensor delivers most of the value, at which point we will tell you that rather than build a model.
Anomaly detection and predictive maintenance
Detecting that equipment is behaving unlike itself is often more tractable than predicting a specific failure, and needs no examples of failures — which matters, because most clients have very few. Training on normal operation and flagging deviation gets useful warning without a labelled failure history.
False-alarm rate is the metric that decides adoption. A detector crying wolf twice a week gets ignored within a month, so we tune to your operational tolerance rather than to a benchmark score.
Forecasting and honest uncertainty
Demand, load and consumption forecasting, with prediction intervals rather than a single number — a point forecast hides exactly the information a planner needs. Where a seasonal statistical model beats a neural network, and it often does on limited history, we use the statistical model.
Every output carries the evidence: which inputs drove it, how confident it is, and when it was last validated against reality. A number with no traceable basis does not get acted on twice.
Questions
What people ask before starting
How much historical data do we need?
Enough to cover the cycles that matter. For seasonal demand that means at least two full seasons, ideally more, because one year cannot distinguish a trend from a one-off. For equipment anomaly detection, a few weeks of normal operation can be sufficient since you are modelling normality rather than failure. If you have three months and want annual seasonality, we will say that up front rather than produce a confident-looking model built on nothing.
We have no failure examples. Can you still help?
Yes, and this is the common case. Anomaly detection trains on normal operation and flags deviation, needing no failure labels at all. It tells you something is wrong rather than exactly what, which for triggering an inspection is usually what you need anyway.
How do you avoid alert fatigue?
By treating the false-alarm rate as a primary requirement rather than a side effect. We agree the tolerable rate with the people who will receive the alerts, tune to it, and include severity levels so a routine drift and an imminent failure do not arrive looking identical. An ignored alert is worse than no alert, because it also trains people to ignore the next one.
Do we need machine learning at all?
Sometimes not, and we will say so. A well-chosen threshold, a rolling average with control limits, or a clear dashboard solves a surprising proportion of monitoring problems at a fraction of the cost and with none of the maintenance burden. We look for that answer first — it is the cheaper recommendation and the one that keeps working without us.
Related
Where this usually connects
Tell us what you are building
One technical call is usually enough to tell you whether this is straightforward, genuinely hard, or the wrong approach entirely. We would rather say so early than quote for the wrong thing.
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