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Analytical Accelerators

Reduce time and risk in analytics projects with preconfigured solutions that deliver results from day one.

Faster results with ready-to-use analytics solutions.

Deploy prebuilt models, dashboards, and reusable components that reduce technical complexity and accelerate time-to-value.
Integrate these accelerators into your existing data and AI stack to lower costs, minimize risk, and enable more accurate decision-making from day one.

50

Less time required to implement analytical use cases

43

Reduction in integration and data maintenance time

5

Faster decision-making in data-driven organizations

Benefits

The value behind the solution.

Accelerated time-to-value

Gain actionable insights within weeks using industry-ready models, features, and dashboards that drastically shorten development cycles.

Lower risk and greater predictability

Rely on proven solutions that ensure traceability, control, and compliance, reducing errors and costs throughout the entire project.

Frictionless scalability

Add new use cases incrementally with reusable components that maintain consistency, reduce costs, and support sustainable growth.

Our methodology

We use a proven approach that prioritizes business objectives and delivers value quickly.

Accelerators integrate seamlessly with your data and systems, ensuring quality, consistency, and lower technical complexity from day one.

With focused training and clear dashboards, adoption starts early. The model then scales through short, measurable iterations that reduce risk and ensure sustainable benefits at every step.

Success stories

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Insights

News, trends and perspectives about Analytical Accelerators.

My journey into AI Governance began at the 2023 Second World Summit of Future Commissions held in Montevideo, Uruguay. This event, hosted by the Parliament of Uruguay and the Inter-Parliamentary Union, was a significant starting point. We discussed the need for anticipatory governance in AI, focusing on how this technology intersects with democracy, human rights, and societal structures. These discussions set the stage for a deeper exploration of AI’s transformative potential.

In the world of data applied to public utilities, striking the right balance between a centralized Business Intelligence (BI) strategy and a self-service approach is key to maintaining agility without sacrificing control.

In recent years, utilities have invested heavily in data analytics as a driver of efficiency and decision-making. However, this evolution has not always been supported by a unified strategy around Business Intelligence (BI) platforms.

In the energy business, the time factor is crucial. Decisions about which plants to start up, when to do it, and at what capacity require careful planning. It is not enough to simply react to real-time consumption: every mistake impacts efficiency and can put the stability of the system at risk.