From 2,000+ Public Health Projects to an AI-Powered Knowledge Platform

GECAP turns a large, fragmented archive of public health projects into a navigable knowledge base, combining structured search, natural language interaction and data visualisation.

Thousands of projects existed as documents and records with inconsistent structure. Professionals could not answer basic questions about what had already been done, where, and by whom.

The challenge was designing for two different kinds of question: precise quantitative queries and open qualitative exploration.

The platform supports knowledge management across a national public health programme, where institutional memory is distributed and rarely comparable between regions.

• Public-sector professionals and programme managers
• Researchers looking for prior work and comparable initiatives
• Institutional stakeholders needing consolidated overviews

Rather than choosing between a dashboard and a conversational interface, the product connects both: aggregate views for orientation, natural language for specific questions, and record-level detail for verification.

The architecture organises projects by programme, theme, territory and time, and keeps every aggregate view one step away from the underlying records.

Users can enter from a question, from a filter, or from a visualisation, and always land on comparable project records.

• Answers always cite the records they were derived from
• Filters and natural language operate on the same result set
• Data visualisations are entry points, not endpoints
• Empty and partial-data states are designed explicitly

AI search is framed as an assistant for exploration. The interface shows what the system understood from a question and lets users adjust the interpretation, keeping the underlying records visible and verifiable.

Shared components for search, filtering, result cards, charts and record detail keep a large information surface visually calm and predictable.

Charts are paired with tabular equivalents, results are keyboard navigable, and colour is never the only carrier of meaning.

With heterogeneous data, the honest representation of gaps and uncertainty is as important to design as the representation of results.