Prediction and optimization
Recommendations and personalization
Rank content, products, or actions around user value, feedback loops, cold start, and controlled experimentation.
Primary buyer: Digital product, growth, and merchandising 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
More relevant user decisions measured through product outcomes
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
Ranking that moves a product metric, not a model metric
Offline ranking scores improve regularly without anything changing for the business. We define the user action worth increasing, handle cold start and sparse feedback honestly, and put changes behind an experiment so you can tell whether it worked.
- Built for
- Digital product, growth, and merchandising teams
- Designed to achieve
- More relevant user decisions measured through product outcomes
What you get
- Target action and success metric agreed first
- Ranking model with cold-start handling
- Experiment setup and readable results
- Guardrails against feedback loops and narrow exposure
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