The intelligence architecture for stakeholder risk

SOLV is a purpose-built intelligence system for structuring stakeholder risk in complex, multi-stakeholder environments. It models stakeholder dynamics, values, and exposure using formal approaches grounded in social-science theory — including Actor-Network Theory and vector-based representations of influence, conflict, and alignment.

Stakeholder interactions are translated into explicit, structured representations. This allows stakeholder risk to be analysed systematically and compared over time — not interpreted anecdotally.

The first challenge is scale and fragmentation. Stakeholder data is dispersed across documents, consultations, media, and public sources — often in volumes no team can process manually.

AI is embedded in SOLV as a perception layer: it extracts and structures data from these unstructured sources. It does not perform reasoning, scoring, or strategic logic. Extracted data passes through checks for relevance, context, and consistency before entering the model. All analysis takes place on structured data, not on free-form text.

Once validated, data is organised into a structured digital twin of the human environment: stakeholders, their positions, relationships, and relative influence — represented as a coherent, queryable system.

The model updates as new data enters. Shifts in positions, emerging actors, and changing coalitions become visible and comparable over time. This is essential during long execution phases where stakeholder dynamics don't stand still.

Risk assessment and scenario comparison in SOLV are algorithmic and reproducible. For any given state of the digital twin, explicit logic produces consistent outcomes — run the same inputs, get the same results.

As additional data is incorporated, the model is refined and outcomes recalculated. Precision increases rather than volatility. This is the core difference with probabilistic AI tools, where outputs vary with every query.

Projects and policies rarely exist in isolation. They sit within broader portfolios where budget, capacity, and political priorities are distributed across multiple initiatives.

SOLV applies the same modelling logic at portfolio level. Stakeholder risk can be assessed and compared consistently across scenarios and initiatives — making it possible to see how risk accumulates, shifts, or concentrates using a single, coherent model.

Stakeholder risk assessments inform real decisions, and scrutiny is inevitable. Every element in the model remains linked to its source data. All inputs, curation steps, and analytical outcomes can be inspected at any time.

Because analysis follows explicit logic rather than probabilistic inference, the full reasoning chain is auditable. Conclusions can be reconstructed and reviewed as data evolves — by your team, by leadership, or by external reviewers.