Alterra Clothes

Alterra works as a textile intermediary in a B2B white-label model. It connects clients looking for large production runs with specialist manufacturers who don't have the structure to handle the whole sales process.

Despite annual turnover of around €150,000, the company is run part-time by its two founders. The model demands constant coordination between clients, suppliers, quotes, production and invoicing, which makes for a naturally complex operation.

As volume grew, information was spread across folders, spreadsheets and scattered conversations. Every new project meant repeating tasks, copying data between files and manually checking critical information. The problem wasn't a lack of data. It was a lack of structure.

Without standardisation, each new order was treated almost as a one-off. Duplicated tasks, frequent accounting errors and real difficulty keeping track of how the business was actually doing. An invisible impact, but a cumulative one: lost time, decisions made without consolidated data and an operation that depended on the founders' memory.

In textiles, every order has its own specifications (materials, quantities, suppliers, deadlines, customisation). That variability created the perception that the processes couldn't be standardised. In practice, what varied were the parameters of each order, not the structure of the process.

The Google Drive structure was redesigned to reflect the real logic of the sales process. Each new client now automatically generates a set of folders and documents that follows that model. This removed one of the main sources of lost time: creating documentation by hand, and inconsistently.

In parallel, we built a financial system linked by project. The data collected in the client's initial form now feeds the quotes automatically. That same data feeds the project sheets, where supplier purchases, costs and margins are managed. Sales information is then synced for accounting. Instead of copying data between sheets, the founders now work on a connected system where each action automatically updates the rest of the model.

The third step was centralising orders in an operational CRM. Whatever channel they come from (email, Instagram or WhatsApp), orders now go through a single point of entry. Each order is checked automatically to see whether it contains all the information needed. When the data is complete, the quote can be generated automatically from past records. When it isn't, the system itself asks the client for what's missing.

The next stage was cleaning up the database: past production runs, prices, suppliers and margins brought together in a single structure, which now underpins both the quoting system and performance analysis.

Finally, with the data in order, we built operational dashboards that show the business in real time. Order volumes, margins, supplier performance and sales trends are now visible at a glance.

Many businesses assume their processes are too specific to be standardised. In most cases, that isn't true. What varies is rarely the process; what varies is the data that goes into it. Standardisation doesn't remove flexibility. It removes improvisation.