Today the shopkeeper decides what to display and what to buy based on whichever product they believe earns them the most. This tool turns that conversation into a numeric exercise: four data points per product —dictated or typed— and the real profit of the competitor versus its Postobón counterpart, in pesos and as a percentage, right in their own store.
Scroll to walk through the visitTap any step to see what happens, what the rep says, and what gets logged for BI.
Purchase price, discount, sale price and boxes per month, for the competitor's product and its Postobón counterpart. The rep dictates it by voice or types it in; price lists come preloaded and are editable in the field.
Not gross sales: the actual profit. Net cost after discount, unit margin and monthly profit for each side, over the same horizon. The difference in pesos and as a percentage appears on a single screen, readable from a meter away.
Point of sale, duration, SKUs compared, data, result and closing survey are all saved automatically. That data feeds the BI dashboard and the Business analytics.
The rep repeats what the shopkeeper tells them. The engine transcribes the audio, a language model extracts each data point, assigns it to the right product and puts it in its field. The rep just confirms. Here's what it looks like:
The shopkeeper believes the product that sells fastest is the one that earns them the most. With real purchase price, discount and sale price, the tool shows the monthly profit of each option, not gross sales.
The competitor's soda isn't sold on its own but bundled with the food item. The module separates food and beverage, and compares the current combo against the same combo with Postobón's Pareto SKU.
Eight data points per exercise, typed on screen in the middle of the store, cost time and attention. Dictated, they take under a minute, and the rep never looks away from the shopkeeper.
Once the first argument lands, the table of the 10 Pareto SKUs with the same assumptions shows how much the month would add up to by expanding the portfolio. Every row is editable.
Four questions at the end: was it closed with the tool, what was achieved, how many boxes got committed? It connects the experience to the commercial outcome.
No manual report: every exercise emits a record with user, point of sale, duration, SKUs, data, result and survey. It's the raw material for the module 4 dashboard.
Product selection, matching, capture, result and expanded portfolio.
Beverage/food breakdown, scenarios and replacement suggestion.
Visit close-out and automatic record, exportable as JSON or CSV.
Closing rate, most-combo'd SKUs, average duration and boxes committed per rep.
Already working in the demo with the browser's engine; in production with Whisper and an LLM.
By default the demo uses the browser's native recognition (Chrome, Edge or Safari, Spanish). If the demo is deployed on your own domain, paste an OpenAI API key here to transcribe with Whisper and extract the fields with a language model. The key is saved only in this browser, never in the code.