Prediction and optimization
Anomaly detection and predictive maintenance
Detect unusual behavior and early-warning patterns in equipment, operations, transactions, or platform telemetry.
Primary buyer: Operations, reliability, risk, and platform teamsThe real constraint
A lower model error is useless when the horizon, intervention, constraints, or economic cost are modeled incorrectly.
How we approach it
We establish a decision baseline, error economics, constraints, uncertainty, override workflow, and retraining trigger.
How we prove it
Time-aware backtests compare the system with naive and operational baselines across high-cost segments.
Production system
Prediction and optimization
Forecasts, scores, recommendations, and optimized plans tied to the decision that consumes them.
- 01Target and decision definition
- 02Time-aware data contracts
- 03Baselines, backtests, and uncertainty
- 04Optimization constraints and scenarios
- 05Decision UI, overrides, and monitoring
Typical fit
Earlier investigation focused on explainable, high-cost deviations
FAQ
Questions before a pilot
These answers describe how we normally work. Only a signed agreement creates commitments. Website enquiry terms
01How much history do we need?
Enough to cover the patterns you care about, which usually means at least two full cycles of whatever repeats: seasons, weeks, promotion periods. With less than that we can still build something, but we will be clear that it is a starting point rather than a dependable forecast. Discovery tells you which of the two you have.
02We already forecast in a spreadsheet. Why would this be better?
It might not be, and that is worth finding out cheaply. We backtest against your existing method rather than against nothing, and if the spreadsheet wins on the segments that matter you have saved a production build. More often the gain is not accuracy but coverage: the same quality of judgement applied to ten thousand items instead of the fifty someone has time for.
03What happens when something unprecedented happens?
The model gets it wrong, like everyone else did. What matters is whether the system tells you it is uncertain instead of stating a confident number. We build prediction intervals and the manual override in from the start, because the plan has to survive contact with someone who knows something the data does not.
Service focus
Alerts people still read after the first month
An anomaly detector that fires forty times a day gets muted, and then it might as well not exist. We tune against your alert capacity and the real cost of a false positive, and every alert carries the evidence that triggered it so an engineer can act rather than investigate the alert itself.
- Built for
- Operations, reliability, risk, and platform teams
- Designed to achieve
- Earlier investigation focused on explainable, high-cost deviations
What you get
- Detection tuned to your team's alert capacity
- Each alert carries its supporting evidence
- False-positive cost built into the threshold
- Integration with your existing on-call or work-order flow
Need a different system?
Not quite your solution?
Explore nearby systems or bring the outcome, workflow, and constraints that do not fit a predefined category. Tandemora can scope a custom solution around the real operating problem.
Your version will have different constraints.
Bring the workflow, current system, available evidence, and the assumption you trust least. We will identify the smallest useful next step.
Discuss this system