Domain intelligence

Scientific and technical literature identifies problems and discusses solutions. But translating this into concrete, context-specific recommendations remains manual, slow, and expensive—dependent on senior profiles who are scarce and in high demand.

Teams review the same literature, adapt similar solutions, and rebuild reasoning from scratch. As knowledge volumes grow, this becomes unsustainable.

SOLV builds a domain-specific action library capturing every documented intervention, measure, or solution from your authoritative literature. This can be proprietary, shared, or made public.

Each action is pre-rated across hundreds of criteria: context fit, stakeholder values, effectiveness, cost, implementation requirements, geographic applicability, and regulatory constraints.

The result is a structured, searchable intelligence base—not a static collection of documents, but a living system that evolves as new literature is added.

When a situation is described, SOLV builds a situational model using location, topic, stakeholder landscape, constraints, and budget.

Based on this model, SOLV selects, ranks, and weights the actions that fit, delivering recommendations that are immediately applicable rather than generic. The intelligence base stays current without manual recuration.

Structured inventory of all documented interventions within a domain, extracted from curated authoritative sources.

• What interventions exist for reducing aircraft noise impact on residential areas?

• Which measures have been documented for improving biodiversity in urban waterways?

• What does the literature say about community engagement in wind farm siting?

Each action pre-rated across hundreds of criteria including context fit, stakeholder values, effectiveness, cost, and constraints.

• Which noise mitigation measures are most effective for night-time disturbance?

• What are the implementation costs and timelines for each option?

• Which interventions work in dense urban contexts versus rural settings?

Automatically generated ranked lists of actions for a specific situation, weighted according to contextual parameters.

• Given our site constraints and stakeholder landscape, which interventions should we prioritise?

• What's the best approach for a project in a politically sensitive area with limited budget?

• If community acceptance matters more than speed, how does the ranking change?

Every recommendation linked back to the underlying literature and expertise.

• What's the evidence base for recommending this intervention?

• Which studies support the effectiveness rating for this measure?

• If a stakeholder challenges this recommendation, where's the documentation?