The Demand Agent reads market data, your own sell-out and consumer research together. It sizes every growth pocket in money, explains why consumers want it and ranks it, so where to play is decided on evidence, not by the loudest deck in the room.
Works on its own or as part of the chain, guided step by step, or autonomously.
Every agent page follows the same illustrative case: a chocolate brand in Switzerland looking for its next growth move. It has market data, its own sell-out, a consumer study, loyalty data and margin data – spread across five teams and five tools. The data is synthetic and the figures are illustrative. We start with one plain question.
Each step is one question to the Demand Agent. The answer comes back validated, with weak evidence flagged and outputs ready for your systems.
Shown step by step – it can also run on its own. The case below is guided: one question per step, with suggested prompts leading to the next. Give the Demand Agent a broader brief and it works autonomously – it plans the analysis itself, goes as deep as the question needs, skips steps that add nothing and comes back with the answer and its full audit trail.
Where the category is moving, and why – category trends, competitor moves and share shifts from market data, explained by what consumers say in your research.
Your sell-out against the market, down to SKU, region and channel – where you lead, where you lose ground, and whether the cause is listing, shelf or rate of sale.
Each opportunity sized in revenue and margin, net of what it would take from your own range, ranked – with who sits inside it.
You don’t need the next agent to act. The ranked opportunities go into business and category planning, opportunity #1 goes into the innovation pipeline, and the key account managers get their listing and stock-gap list the same day.
The three steps above are run by specialised sub-agents. We tune them to your categories, data and ways of working – and build new ones when your teams need them.
It carries the context across and suggests the next three questions. Run them, and the Consumer Agent picks up exactly where this one left off.
Every recommendation is computed by statistics and machine learning, validated, and traceable to the data cell it came from. The language model picks the variables and writes the explanation; it does not calculate the numbers.
A 30-minute live session on prepared data: the agent interprets your question,
runs the analysis and returns a validated answer you can drill down to the data cell.
