About eZap

Built in Porto for a gap in grid operations

European distribution operators carry reserve margins of 15 to 25 percent above peak demand. eZap helps replace fixed assumptions with timely, feeder-level forecasts.

How we started

The problem came before the company

Distribution grid operators across Europe carry standing reserve margins of 15 to 25 percent above peak demand. Some of that margin supports genuine reliability. A significant portion remains because accurate intraday forecasts are unavailable, too coarse for feeder-level planning, or delivered too late to guide action.

The cost is real. Reserve energy that is procured but not dispatched still appears on balancing invoices. Over-scheduling creates a direct operational cost across every feeder, every day.

Before founding eZap in 2024, Nuno and Tiago had both worked in the energy sector. Nuno led operations planning at a regional distribution operator, while Tiago built forecasting pipelines at an energy analytics company. They saw the same gap: the data existed, but operators lacked tooling to turn it into actionable feeder-level forecasts on short timescales.

eZap was built to close that gap.

eZap team working in Porto office
The team

Who builds eZap

Nuno Sobral, CEO at eZap

Nuno Sobral

CEO and Co-Founder

Before starting eZap, Nuno led operations planning at a regional distribution operator. He saw reserve over-procurement from the inside and set out to give planning teams an actual forecast instead of fixed percentages. He focuses on customer relationships and product direction, keeping the product grounded in real operator needs.

Tiago Ferreira, CTO at eZap

Tiago Ferreira

CTO and Co-Founder

Before co-founding eZap, Tiago built demand forecasting systems at an energy analytics company. He designed the probabilistic forecasting pipeline, SCADA integration layer, and dispatch optimization module. He focuses on model accuracy and API production reliability under real grid operating conditions.

Ines Carvalho, Head of Data Science at eZap

Ines Carvalho

Head of Data Science

Ines applies machine learning to time-series problems in energy and infrastructure. At eZap, she leads model development and validation. Her work covers forecast accuracy benchmarking, per-feeder model architecture, weather feature engineering, and the monthly accuracy reports provided to customers.

Backing

Angel-backed, operator-focused

eZap closed a EUR 750,000 angel round in February 2026 with investors experienced in European energy infrastructure and B2B software. The funding supports production platform completion, paid pilots with operators in Portugal and Spain, and data science team expansion.

We are not pursuing platform growth or consumer scale. We are building a technical product for a specific operator audience and taking the time to build it correctly.