A B2B distributor gains from AI at three points: structured order intake, demand forecasting per customer and product, and delivery routing. The order matters. Without orders recorded as data, there is no history to forecast and no reliable address to route to. That is why the first project is usually the simplest: getting the order out of the spreadsheet.
Distribution is a business of thin margins and daily operations. The owner knows every customer, the buyer knows by heart what each restaurant orders on Tuesdays, and the driver already has the route memorized. That knowledge works until the day the company grows, someone goes on vacation or a large customer changes its pattern. AI for a distributor is, in practice, turning that knowledge into data the operation can use without depending on one person.
Is taking orders over WhatsApp a problem?
No. The problem is the order that arrives over WhatsApp and never becomes data.
The RDR Distribuidora case shows why the channel should not be replaced. RDR delivers fruit and vegetables to restaurants, markets, industrial kitchens and consumers in São Paulo and its metro area, and takes most of its orders over WhatsApp. When we built the site, the decision was not to create a parallel channel with a cart and sign-up that a restaurant customer would not use. The site answers where RDR delivers, what it sells and how to order, then sends the visitor into the chat. The contact form already asks for the customer type, and RDR commits to replying within 2 hours during business hours.
The channel stayed where the customer is. What is missing, in almost every distributor that operates this way, is the next step: someone reads the message, types the order into the system or spreadsheet and checks stock and price. It is repetitive work, prone to typing errors and invisible to any analysis.
An AI agent at that point reads the customer's message, identifies the items, quantities, unit (box, kilo, bunch), delivery address and requested time window, and returns the structured order for a person to confirm. It does not replace customer service. It takes the typing out of the way and creates the record the other two projects need.
How do you forecast demand for perishable products?
Fresh produce is the hardest demand forecasting case, which makes it a good test. The product lasts days, prices move with the harvest, and restaurant customers order according to the week's traffic. Erring high becomes waste; erring low becomes a customer without product.
What changes compared to ordinary retail forecasting:
- The horizon is short. Forecasting next week is worth more than forecasting the quarter. The model runs often and corrects itself quickly.
- The unit is the customer, not only the product. In B2B, a few customers concentrate most of the volume. Forecasting each large restaurant's order, by day of the week, is more useful than forecasting total tomatoes.
- The weekly pattern dominates. Restaurants order for the weekend, industrial kitchens order for Monday's production. RDR already assembles weekly kits for restaurants and households, which is a way of standardizing that demand before any model.
The first version of the model can be modest: for the customers that buy the most, the expected quantity per item for next week, with an error range. The buyer compares it with what they would buy on their own. If the model errs less, it starts guiding the purchase. If it errs more, the data is incomplete, and that is a useful answer too.
Is routing worth it for a small fleet?
It is, when the delivery area is known and customers' time windows are strict. RDR covers the five zones of the city of São Paulo and cities such as Guarulhos, Osasco, Barueri, Santo André, São Bernardo and Diadema. Restaurants receive before lunch; markets receive early. With that combination, the order of the stops changes the cost of the day.
Routing does not need to start with a full optimization system. The first version groups the day's orders by zone, sequences the stops within each zone while respecting time windows, and splits them across vehicles. That alone reduces kilometers driven and late deliveries. The more sophisticated model, which accounts for unloading time, traffic and capacity per vehicle, comes in once the basics are running and each customer's address is clean in the records. Here the dependency on the first project returns: without structured orders, the address comes in wrong and so does the route.
| Process | How it is done in the spreadsheet | With data and AI | Metric to track |
|---|---|---|---|
| Order intake (RDR: WhatsApp, form or Instagram, 4 steps to delivery) | Someone reads the message and types item, quantity and address | Agent structures the order and the person only confirms | Response time (RDR: within 2 hours during business hours) and errors per order |
| Purchasing and demand forecasting | Buyer estimates from memory and experience | Weekly forecast per customer and per item, with an error range | Model error against the manual estimate; perishable waste |
| Delivery routing (RDR: 5 city zones and 6 metro-area cities) | Driver sets the sequence from memory | Grouping by zone and stop order respecting time windows | Kilometers per delivery and deliveries outside the window |
| Business analysis | Report assembled by hand at month end | Single database with orders, deliveries and customers | Time to answer "how much did we sell to customer X" |
| Validating each project | Does not exist; the process changes and you see what happens | 3 to 6 week PoC with a fixed deadline and success criteria | Go or no-go decision at the end of the period |
Where this data lives
All three projects depend on a single base: orders, customers, deliveries and purchases in the same place, with history. For a distributor, this rarely requires the data architecture of a large company. A well-modeled relational database, fed by the order agent and the invoicing system, covers most cases. The choice between a data warehouse and a data lake, and when each makes sense, is in the post on data lake or data warehouse in 2026.
The biggest risk is not technical. It is the forecasting model looking good in the presentation and never entering the buyer's routine. That happens when the model is built far from the person who buys, with no comparison against the manual estimate and no simple way to check the result every day. We cover the causes in why machine learning models never reach production. In distribution, the practical rule is: if the buyer does not open the model's output before closing the order with the supplier, the model does not exist.
Cortex IQ's data and AI service starts with that design: which data the operation already produces, which is missing, and which project comes first so the other two have a foundation.
Next step
If your distributor takes orders by message and buys from experience, the first project is turning the order into data; forecasting and routing follow. The distribution and logistics page shows what we have already done in the sector, and the data and AI page explains how we structure the base for all three projects.