Step 02 · Prediction Engine
In progressWhat will happen?
Estimate demand elasticity per pack, per channel.
Elasticity and cross-elasticity models (including cannibalisation between own packs) with confidence intervals, so a price move can be scoped before it is made.
demand
price →
Terms on this page
- Elasticity
- How much demand for a pack changes when its price changes.
- Cross-elasticity
- How much demand for one pack changes when another pack’s price changes.
- Cannibalisation
- One of the business’s own packs taking volume from another.
- Confidence interval
- The range the true value is likely to fall within.
How the Prediction Engine gets there
- 01Three inputs: results already surfaced by the Analytics Engine, the underlying database, and the user’s prompt.
- 02Our proprietary agentic structure reads the shape of the data (task type, target, features, volume) and composes a prediction specification.
- 03It selects candidate models, or an ensemble, from a curated catalog, then runs a real ML workflow: preparation, training, validation, held-out testing.
- 04The lifecycle stays prompt-driven: promote a model into production, wire it into a dashboard, schedule re-forecasts and alerts.
In progress. Under active development now: agentic AutoML with calibrated, explainable output for real business prediction targets.
Explore the Prediction EngineWhat changes
The relay today
Request routed to a tech lead, then a data team: gather, clean, build, validate, ship. Weeks or months pass.
With Theovya
A trained, validated model with honest metrics, while the question is still relevant.
This is the decision most FMCG businesses make once a year, in a room, with a spreadsheet. It can be made continuously, with the trade-offs computed instead of debated.