Backing Electric Twin: The Future of Decision Intelligence is Synthetic

Mercuri is pleased to announce our continued support for Electric Twin, participating in its $10m seed round led by Atomico, with additional follow-on from LocalGlobe, alongside a16z co-founder Marc Andreessen and leading operators from Slack, Kantar, Palantir, and Entrepreneur First.

Electric Twin is an AI-native company redefining how organisations simulate and understand human behaviour. Its platform enables enterprises not only to interpret social attitudes, but to anticipate decisions, with scientific rigour, longitudinal depth, and operational relevance, 10,000 times faster than with traditional methods, significantly reducing costs. 

Electric Twin delivers on three pillars of value:

1. Customised Audience Models for Business 

Critical Tasks
At the core of Electric Twin’s platform is a dynamic audience modelling engine that harmonises survey data, proprietary enterprise data and alternative external inputs into high-fidelity synthetic populations. These audience models - benchmarked at over 95% accuracy across 155+ countries - can be queried, segmented and redeployed across use cases spanning market research, product, commercial strategy and marketing.

2. Re-Architecting Enterprise Workflows with Agentic Systems

Electric Twin aims to change how enterprises operate. Its audience models are not static but evolve with the business and real world events, and the platform's agentic workflow enables dynamic re-interrogation and real-time simulation, re-quering, segmentation, and repurpose of models across business functions, transforming research from a one-off activity into a longitudinal, always-on, strategic capability across an enterprise. In effect, it is re-architecting the enterprise stack for a generative-first future, where decisions are data-driven and collaborative.

3. Predictive Infrastructure for Data-Driven Decision-Making

Beyond operational efficiency, Electric Twin over time aims to become a strategic asset. Its models integrate deeply into enterprise data infrastructure, providing predictive insight into business-critical KPIs such as customer acquisition cost, retention dynamics, pricing sensitivity, and scenario forecasting before decisions are made. This is a platform-level rethink of how teams engage with data and make data-driven business strategy decisions.

Core to Electric Twin’s approach is its scientific rigour. In a landscape where many generative products suffer from hallucinations or superficial implementation, Electric Twin is purpose-built for enterprise-grade reliability. Accuracy is the lead KPI, focusing on performance, repeatability, and trust.

Unsurprisingly, Electric Twin’s unique value proposition is translating into real-world momentum. The company is firmly on the revenue J-curve, signing six-figure contracts with enterprise customers and governments, with their recent partnership with The Times illustrating the scale and versatility of the platform.

Underpinning this success, and core driver of our investment, is the team. Dr Ben Warner served as Chief Adviser on Digital and Data to the Prime Minister during COVID-19, where he experienced first-hand the consequences of making high-stakes policy decisions without the ability to rapidly test and understand population behaviour. Alex Cooper is a former military commander who established the UK's mass testing response during the pandemic, giving him operational experience in rapidly deploying complex systems under pressure at national scale. Together they’ve built a top tier multidisciplinary team around them with clear focus, technical strength, and execution velocity. This is exactly the kind of team we want building foundational infrastructure for the AI-native enterprise.

A Natural Fit for Mercuri’s Thesis

Electric Twin fits directly into Mercuri’s thesis on generative media: that generative AI is not just a creative tool, but a full-stack transformation layer, reshaping how companies create, distribute, consume, and monetise content, insight, and strategy. Synthetic data, simulation engines, and agentic tools are fundamental to this shift. Electric Twin exemplifies that vision: using AI not to produce content, but to simulate behaviour, anticipate decisions, and drive enterprise strategy. And to do that whilst redefining research economics.

We’re delighted to continue supporting the Electric Twin team on their journey.