Software born in the field.

A web platform that looks at spend at every level of detail, from the overall picture down to the single item, following the company’s own spend model.

Artificial intelligence sorts spend into the main categories, by your own criteria or a standard one, tidying up errors, differences of language and incomplete entries as it goes. Through the Semantic Engine, TSV recognises the same item even when it is described in different ways. The views can follow an industry standard such as UNSPSC, or be set up around what the company actually needs.

A natural-language layer over the existing applications: someone asks a question and gets an answer drawn from the real data, together with the technical record of the query that ran.

The back office reaches the information without knowing how the applications are built and without going through the system experts. For IT, every interaction is logged and every answer can be checked and reproduced later, fully in line with compliance and banking secrecy requirements.

frAIme brings together data from different systems and opens it up to AI and machine learning models, without necessarily moving it from where it lives. The data fabric is built on the infrastructure already in place and grows with what the company needs.

There is no need to gather everything into a new data lake. The cloud-native architecture connects the data where it already sits — in the management systems, on the mainframe, even in the systems of a company that has just been acquired — and makes it available for analysis, machine learning and AI applications.

When a company first turns to artificial intelligence, the hard part is working out how to apply it to the way people actually work. Questions come up about the openings, about whether the data is there, about compliance, and about which use cases make a sensible place to start.

Officina AI is a hands-on day on your site, not a lecture. The work is on your own processes: the groundwork of generative and agentic AI applied to your sector, a map of the processes that cause the most friction, use cases generated with a guided canvas, and those use cases ranked on an impact/feasibility matrix.

The day turns the openings you find into concrete direction for how the processes could change. The ideas that come up are explored and translated into possible AI applications, laying the ground for what might be built next and for fitting it into day-to-day operations.