POS, Inventory and Loyalty Integration: A Real Case

“Connecting systems” sounds like a weekend job: read each API’s documentation, write the code that ties them together, done. In practice it’s almost never like that. In any POS, inventory and loyalty integration, the hard part isn’t the connection itself. It’s everything you have to solve before you can trust the data that connection brings in. Here’s how we built the data infrastructure for a real restaurant business in Spain, connecting three systems that didn’t talk to each other.

The real problem isn’t technical, it’s trusting the data

The business used Ágora as its point of sale, GStock for inventory, production and purchasing, and Cheerfy as its loyalty system. Each had its own API, so in theory connecting them was just a matter of writing the right code.

In practice, each system represented the same information differently: the same product under slightly different names on each platform, dates in different formats, duplicate records when a sale appeared in more than one system. Without solving that first, any report combining data from all three would be wrong, even if the technical connection worked perfectly.

The four parts of the work

1. API connections to each system. Ágora for sales data. GStock for recipes, production runs, waste, inventory, purchases, incoming delivery notes and invoices. Cheerfy for CRM data, email campaigns and the purchases of customers enrolled in the loyalty program.

2. Data cleaning. Unifying how each product is named across systems, normalizing dates and formats, and removing duplicates.

3. Pipelines. Automated processes that keep everything up to date, so the information doesn’t go stale the moment nobody is checking it by hand.

4. A data infrastructure the business owns. Not us: an asset for the client, connected to the same three systems it uses every day.

The part that really matters isn’t the technical connection to each API; that’s relatively straightforward. What makes or breaks a project like this is cleaning and normalizing the data. Without that, you have a connection that works but brings in unreliable information, and no report or tool built on top of it is worth anything.

What you can build next

Once the data is reliable and in one place, building new things on top gets much simpler and faster. In this project, that foundation made it possible to build a dashboard bringing the business’s information together, a purchasing and production tool, and to connect the same infrastructure to an AI model through an MCP server so the team can ask how the business is doing in plain language. We tell that half of the story in our AI copilot case study.

Clean sales history is also the starting point for anticipating demand. If that’s your next question, read demand forecasting for small business. The full project, La Diez, is in our work.

FAQ

Can this be done with any system, or only the ones this business used?

With any system that offers a way to get data out, whether through an API, an export or another method. The systems in this case are an example, not a requirement.

What happens if a new system is added later?

It connects to the existing infrastructure; nothing has to be rebuilt from scratch.

Do I need an in-house data team to keep this running?

No. The infrastructure is built to run on its own once it’s set up. Occasional tweaks come up, not daily work.

Where to start

If your business has information scattered across systems that don’t talk to each other, and you want one reliable place to check it, this is exactly the kind of project we build as part of our custom software development services. For the bigger picture, see our custom software development guide. Tell us which systems you use today and we’ll see how quickly that information can be organized.