HorizoonX × Datadom: Building the AI Engine Behind a F&B Super-App

In just a few years HorizoonX, a Riyadh-based startup, built a full-stack cloud platform that lets restaurants, cafés, and cloud kitchens across Saudi Arabia run their entire operation from a single app: 

• POS

• Inventory

• Ordering

• digital menus

• Payments

• supplier sourcing

• even delivery logistics. 

The team at HorizoonX knew that dashboards and reports would only take them so far. Their platform would be sitting on a growing mountain of operational data from transaction histories, inventory movements, all the way to promotional activity. They wanted that data to be working for their customers from the start and making it immediately useful for restaurant owners who are busy running a business. 

A great project for the datadom team!

When we started working together, we spent time understanding not just HorizoonX's technology, but what their users actually wanted. 

A restaurant operator in Riyadh doesn't need a generic AI chatbot. They need to know that they're about to run out of chicken before Friday rush, that latte sales have quietly doubled over the past two weeks, that the walk-in cooler is drawing more power than it should, or that the new cappuccino promo has no signage up in three of their locations. 

Together with HorizoonX we identified four operational use cases that would be great AI features to get started building agentic systems: 

• stock-out forecasting

• sales trend detection

• resource usage monitoring

• signage visibility tracking

• automated signage creation

Rather than disappearing into a lab for months, we designed a first phase that could prove value fast. 

HorizoonX was already running on Snowflake, so we prototyped directly on their existing environment using Snowflake Cortex. Quite quickly, we had working demos that could generate natural-language insights from operational data like flagging low-stock items alongside supplier lead-time risks, summarizing week-over-week sales movements in plain language, catching off-hours utility anomalies, spotting gaps between active promotions and what was actually displayed in-store and even automatically generating tasteful signage promotion image that would fit large screen.

This architecture was designed so these monitoring modules could run independently and be augmentable with an orchestrating agent in later phases that can pull in the right function in a conversation.

By the end of the engagement, HorizoonX had a validated AI strategy, a working MVP across all four monitoring use cases, and a clear technical path toward the conversational and agentic capabilities that will define the next chapter of their product. 

What we're most proud of is that the roadmap and MVP fits them. It matches their product, their users, their stage, and their ambitions. It's something they can keep building on confidently as they scale in their market.