Maximal impact, minimal risk
Maximal impact, minimal risk
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.
Solv matches what investors want with what the environment needs
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 predictive analytics for spatial investments
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’.
What are the benefits of using Solv?
Transform
project resistance into dialogue & support through smart participation
Collect
continuous intelligence about your spatial investment
Accelerate
change by avoiding delays and cost overruns
Simulate & adapt
your spatial investment fast with deep understanding of financial returns, environmental impact and spatial fit.
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.
The process
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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).
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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.
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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.
Successful case studies
Successful case studies
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.
Project consortium: ORG www.orgpermod.com with common-ground.eu and arup.com.
Success rate: 100%
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.
Construction to start soon.
Project consortium: MIT CAU + orgpermod.com and zus.cc + urbanisten.nl
Success rate: 100%
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:
Connect
info@solv.world
Please note that Solv is designed for large spatial development (min 25mio €). Detailed information will be provided on a confidential basis to selected parties only.
