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.
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.
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.
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.
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.
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.
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.
Antwerp | Oosterweelverbinding
A highway completion around Antwerp had been halted due to local protests for decades. By adding parks on top, along with substantial public transit and bicycle infrastructure investment, the project is now broadly supported and construction is underway.
Brussels | Uplace
A major shopping project near Brussels was halted for 15 years due to protests. Through dialogue and interviews with friend and foe, a new project was conceived. A permit has been granted although there is some residual contestation.
Coalition and Concept: www.orgpermod.com.
Success rate: not sure, for now 50%
NY | The meadowlands
The meadowlands area outside of NYC is a major flood zone, suffering from a two-decade stand-off between environmental organizations and the chamber of commerce. This project definition became a major bargaining point between both parties for flood protection, environmental and economic growth.
Amsterdam | IJ
The city and the national government have disagreed for years about how to connect both sides of the IJ water body in Amsterdam. After a year-long process of interviews, sketches and calculations, all parties arrived at a new long-term plan for usage of water and city, involving tunnels and bridges.
Project commission: orgpermod.com + Maarten Schmitt + Larissa Van Der Lugt.
Agreement signed: 100% success rate.
Solv is looking for pilot projects
Solv is a machine learning system. It has been designed around a methodology that has successfully been implemented on several large spatial projects during recent years. In order to further improve and refine our model, we are looking for a number of additional projects.
Please provide us with some additional information about your project to apply:
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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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