SOLV applies a consistent intelligence logic to structure stakeholder complexity into risk-reducing insight. The same logic operates across projects, policies, and investment contexts, independent of sector or role.
SOLV starts from fragmented stakeholder material. Project documentation, consultation input, submissions, and publicly available data are translated into structured information that can be analysed, compared, and reused. This turns dispersed knowledge into a coherent intelligence base, without forcing teams into premature interpretation.
Technical foundation of SOLV
Structured input is organised into a formal social model representing stakeholders, their positions, values, relationships, and influence. Rather than listing actors individually, SOLV captures how interests interact, where tensions concentrate, and how alignment or opposition takes shape across the wider system.
SOLV for stakeholder managers
Proposed actions, policies, or project scenarios are assessed against the social model. SOLV makes visible how different choices are likely to be received, where resistance may emerge, and how coalitions could form or shift. This allows teams to test decisions against stakeholder reality before committing to a course of action.
Use-cases
Insights are translated into decision support with explicit links between stakeholder input, analysis, and outcomes. Rationale remains documented and revisitable as contexts evolve, allowing decisions to remain defensible under scrutiny, across long project timelines and changing governance environments.
SOLV for investors
SOLV embeds AI as a controlled data-extraction layer. Unstructured input is processed through multiple, purpose-specific checks for relevance, context, and data quality before anything enters the system. AI output is never treated as insight in itself.
What SOLV produces is structured intelligence. Extracted data are organised into a persistent social model that represents stakeholders, positions, values, and relationships explicitly. Analysis and comparison happen on this model, ensuring results are consistent, explainable, and traceable to source material.
The same core method supports different responsibilities and decision contexts.
