The Sales Agent finds where the opportunity can actually be won: which channels, accounts and stores have headroom, why they under-perform and which shoppers walk through each door. Then it tells the field what to do there, before the quarter is gone.
Works on its own or as part of the chain – guided step by step, or autonomously.
The Demand Agent sized the opportunity at CHF 2.1M margin. The Consumer Agent turned it into three target groups and four audiences – 9,100 contacts, now in the CRM. It handed over the shoppers, store by store – and three questions for the Sales Agent.
Each step is one question to the Sales 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 Sales 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.
Under-performance by region, channel or SKU, attributed to its causes – assortment, availability, price or competitive pressure – and explained by the shopper behind the gap.
Unused headroom and execution gaps per store, tagged with the consumer groups each outlet serves – so the action fits the shopper, not just the shelf.
Promotion response by channel and out-of-stock risk, read together – so trade spend goes where the shelf can deliver it, and you know which shoppers you lose when it can’t.
The outlet list is sent to your field-force app with the whole trade programme worked out: the action for each store, the shopper story behind it, the scheme, the materials and the order. The rep walks in knowing what to do and why.
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 Portfolio 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.
