What we do · Practice

Data.

We bring together the data held in legacy systems and make it count, building platforms that make it easier to analyse and to use for reporting and AI applications.

Let us talk The services

Where it starts

Data quality is where every AI project begins.

Company data is usually scattered across different systems and legacy archives. With an architecture built on the data fabric principle, we bring it together and make it reachable without necessarily moving it from where it lives, creating a base that reporting and AI applications can actually use.

The approach

Five principles behind the way we work.

They guide the choices we make, from handling the data to building the architectures that support business processes.

Consistent data you can trust

Reachable and interoperable across systems

Traceability and compliance

Scalable in cloud and hybrid environments

Data ready for AI models

The services

From migration to new architectures.

Data migration
& integration

We run complex migrations between unlike systems, handling the transformation and reconciliation of the data. One job can involve hundreds of transformations and several business areas inside a single application, as with the NPL portfolios migrated after an acquisition.

The migration machine

Data platform
& warehouse

We design data hubs and integrated platforms that make a company’s information reachable and consistent, giving everyone one shared, consolidated, traceable base to query for reporting, analysis and models — through the business intelligence tools already in use as well.

Cloud data
architecture

We design cloud and hybrid data architectures that work with the systems still running on-premise. We treat the move to the cloud as a chance to rethink the processes, not merely to shift them, as in the vehicle catalogue project for an automotive group.

The products of this practice

frAIme

Data architecture in the service of AI: the data fabric brings every source together while leaving the data in its own systems, and opens it up to machine learning algorithms.

Case studies

Banking