For food and beverage manufacturers, KINTO turns invoices and customer conversations into sales systems: segments of who buys, an agent that takes orders and a site that works across markets.
The published cases here come from a Colombian processed-food manufacturer and a beverage brand entering the United States.
A food manufacturer usually sells to two kinds of customers, businesses and households, through a mix of a standard online store and a very busy WhatsApp line. Orders arrive by hand, without invoicing data, and delivery dates depend on route and cutoff day. The sales team is stretched, and automation promised to fix this has sometimes made it worse: in the manufacturer’s case, earlier attempts had quoted wrong delivery dates.
A brand with a differentiated product has a different gap. The beverage brand in the second case sells a product chosen for its attributes through retailers, but its website showed a catalog it could not sell from, and replies to customers were manual.
In the food manufacturer case, KINTO started by reading a year of invoices, not by writing a bot. The segmentation showed which customers drive volume and which were going cold. Then it built a WhatsApp sales agent whose delivery date is computed by code, an outbound AI calling pilot and a delivery-route lookup, plus a headless store that preserved the URL structure.
In the beverage brand case, the work was a bilingual, English-first site with US search optimization, after reconciling two diverging copies, with image weight cut by about 73 to 95% depending on the stage.
The manufacturer agent’s conversion figure, about 1 in 4 conversations ending in an order, comes from KINTO’s own commercial proposal and is labeled estimated; KINTO has not audited how it was calculated, and the new store’s cutover was still pending. The calling pilot closed orders in tests but no real campaign was launched. The beverage brand’s case is published as partial: the store and the assistant remain proposals, and there are no commercial results to report.
KINTO’s first move with the food manufacturer was to read the data before proposing anything. It segmented a full year of invoices by repeat frequency and route, reviewed real conversations, including one lost sale, to turn each error into a testable rule, and audited every URL before migrating the store. The signals from the market side were already there: ads were running, with over a third of sessions coming from them, and branded search dominated, which meant demand existed and the leak was elsewhere. Three in four visits came from mobile.
For the beverage brand the starting point was different: a complete internationalization and gap audit, two rounds of corrections from the client, and an SEO audit for the US market and for AI answers. The proposal that followed rested on three frictions: a catalog you cannot buy from, manual replies and sales not measured by channel.
For the agent method, see AI agents for WhatsApp. For the analysis step, see product hacking and growth.
| WhatsApp & voice agents | Agentic ERP | Headless e-commerce | Agentic websites | SEO & GEO | Product hacking & growth | |
|---|---|---|---|---|---|---|
| Food and beverage manufacturing | 1 | 1 | 1 | 1 | 2 | 2 |
Each number counts published case studies.
WhatsApp & voice agents
By analyzing a year of invoices, KINTO found that fewer than half of a food manufacturer’s customers drove almost all of its volume; we built a production WhatsApp agent, an AI calling pilot and a headless store that kept every URL.
Agentic websites
KINTO merged two diverging versions of a beverage brand’s site into a single bilingual, English-first codebase, with SEO and FAQs built for US search engines and AI answers.
By segmenting them. KINTO analyzed tens of thousands of invoice lines over 12 months for a food manufacturer, by repeat frequency and route. About 45% of its customers accounted for about 85% of volume, and a long tail of customers had stopped buying, which showed where the sales effort should go.
Only if code, not the language model, computes them. Delivery dates depend on cutoff times, routes and holidays. In the manufacturer's WhatsApp agent, code computes the date and, if the model contradicts the tool, the answer is regenerated. The agent was tested against dozens of simulated customer scenarios before every release.
By auditing every URL first. In the food manufacturer's headless store, about 98% of the URLs stayed on the same path through the migration. The store was ready for cutover with no lost URLs; the cutover itself was still pending when the case was written.
In a beverage brand's case, KINTO merged two diverging site versions into one bilingual base: English first, Spanish as the second version, product pages matching the real label, US search optimization and FAQs for AI answers. A direct store and a messaging assistant were proposed but not implemented.
Tell KINTO what is going on. The first conversation is to understand it, not to sell.
