Maximal impact, minimal risk

Maximal impact, minimal risk

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Harness the power of enriched collective intelligence

Our world needs impactful large projects in city-making, landscaping, water, energy and mobility to succeed. The reality is that many of these projects never make it.

Solv is a unique machine learning method, significantly improving the chances for success of mediators and managers when pursuing large spatial projects. Solv accelerates the impactful transformation processes our planet needs. It blends AI with human mediation.

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Maximal impact, minimal risk

Solv understands the world as a space of different values. By combining the values of seemingly irreconcilable stakeholders, it transforms conflict into collective intelligence with a combination of data, machine learning, empathetic personal understanding, and technical insight. 

The results maximize the benefits of an investment over a wide community of stakeholders. By doing this, Solv helps reduce the nebulous risks and (often potential conflicts) associated with any large investment. It helps align investment and environment.

In a wider sense, Solv eliminates unnecessary conflicts by finding the path to the highest common benefits solution between conflicting parties. It describes the cost of such a solution in relation to the risks (and costs) of the sub-optimal solution. Maximum impact for minimal risk is not a question of intuition alone. Quantitative intelligence plays a role.

A strategic forecast and analysis tool for large investment projects

Impact and risk are two sides of the same coin. By transforming a project to address the values of stakeholders, the risk of opposition decreases and the positive impact on environment and society grows.

Complex

Large investments are too complex for any single brain. They require enriched intelligence because the trade-offs can never simply be reduced to an econometric cost-benefit analysis.

Balance

On the contrary, this is about a balance between forces, which operate based on differing values and principles. Environmental and social impact is, by definition, at least partially a question of what its stakeholders prioritize.

Predict

Eliminating unnecessary risk is key for large investments. There exist substantial unknown risks, which have to do with the people (spatially) surrounding the project: the stakeholders. For the first time, Solv now makes it possible to measure, quantify, predict and mitigate these ‘known unknowns’.

How does it work?

Solv offers dynamic analyses, reports and strategic options for investors and stakeholders. 

Experienced mediators and process managers carry out in-depth interviews of the stakeholders in order to build a value space. They have been certified to work with the Solv system. The inputs are combined with existing anonymous datasets. Algorithms and machine learning processes generate optimal path outcomes.

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The process

  • 1. Situational analysis

    The first report is a situational analysis of the stakeholder field, identifying values, tribes, and battlegrounds in relation to a given project (or investment). In short, this analysis defines a value space populated by all relevant stakeholders, relative to a specific investment. It also offers an assessment of the flexibility of the proposed project (and therefore of chances of success).

  • 2. Project satisfaction

    The machine recommends a project satisfaction effort. An analysis of which interventions are required to achieve consensus. In some cases, a stakeholder alignment may be required. This module assesses the possible interventions in the value space and makes recommendations for specific trajectories with specific groups of players.

  • 3. Selection of optimal scenarios

    The strategic forecast tool generates a selection of optimal scenarios, each with a different proportion of risk to cost, and with an adjusted ESG performance metric for the selected options.

    Solv identifies the path of minimal risk and maximal impact. It calculates the optimal scenarios with complex algorithms and machine learning through simulation of different risk/cost/impact scenarios, combined with ESG performance and recommended stakeholder alignment scenarios.

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Please note that Solv is designed for large spatial development (min 50mio €). Detailed information will be provided on a confidential basis to selected parties only.

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