A simulated population of AI personas, calibrated to how people really buy, so you can test decisions against your customers before making them.
Sales forecasts have traditionally looked backwards. They've tried to identify patterns in the sales and hoped they'll continue into the future. For range planning decisions months or even years in advance, they perform poorly. Find out why here.
Frontier AI has read how different personas tend to act. By fine-tuning our own custom models we can mimic how personas who might shop at your store are likely to behave with remarkable accuracy. Find out why it works here.
The output of our societal models is integrated into more traditional ML-based methods to deliver hyper-accurate predictions of future demand.
Additionally, as our models understand the behaviour behind the sales, they offer explainability and adaptability via natural language. You can ask it what's driving that prediction, which personas tend to buy which products and how different scenarios change the predictions.
Most business decisions are a bet on how consumers will react and behave. These are the areas we're targeting.
Inform design decisions before anything is made. Show the population a set of designs and it tells you which it prefers, by how much, and who chose each one.
Pricing works the same way. Change the price on one design and see what moves: how many still choose it, who trades down, and what that does to volume and margin.
Test the impact of a campaign on the groups you're targeting, then change and improve it before you launch. A message doesn't land on a population evenly: some groups move, most don't, and the model shows which is which while there is still time to act.
Modelling starts hyper-local: a catchment, a town, or a region, each its own population with its own concerns.
Those populations fit together into broader groups, a country or a market, so the model that describes one high street can also show how a change lands across an entire population.