Ozyntel

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Lily Pepper
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Charline Baker-Friesen
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Alex Baker-Friesen

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Ozyntel

The Mission

Our mission is to make sustainability transformation steerable by building a socio-technical digital twin of an organisation’s transformation dynamics. We model structural and human factors together (processes, governance, incentives, stakeholder dynamics, culture, change capacity) so leaders can simulate scenarios, forecast likely outcomes, and intervene earlier at the right leverage points. This reduces wasted effort, burnout, and stalled programmes, while helping organisations build internal capability rather than relying on perpetual consultancy.

The Challenge

Up to 98% sustainability programmes fail or stall. This because organisations cannot see (or test) the systemic and human dynamics that shape implementation, and consequently cannot know the consequences of a decision or change. Today’s tools mostly report past performance. They do not provide a forward-looking way to model transformation risk, stress-test decisions, and identify where bottlenecks, misalignment, and resistance will arise before costs compound. This failure is costing companies hundreds of thousands to millions of euros, and causes burnout, employee turnover, reputational risks, and more.

The solution

We provide a digital twin-based diagnostic and decision intelligence platform for sustainability transformation, grounded in our own transdisciplinary methodology (systems thinking, transformation science, behavioural science, governance, and learning design). The platform captures organisational context via different data sources, such as documents and structured prompts, builds a working model of the organisation’s structural and human dynamics, and enables scenario simulation to forecast consequences and trade-offs before decisions are implemented. Technically, it combines an AI/ML analytics engine (our core innovation) that performs diagnostics and scenario-based forecasting with scientific guardrails, and an LLM interface layer that makes the outputs intelligible and actionable through natural-language guidance and decision artefacts.

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