Some partnerships begin with a shared conviction before they begin with a collaboration. RANA and Apurva.ai are grounded in the shared belief that communities are not subjects of change, but their authors.<\/p>\n\n\n\n
Meet RANA (Resilience Action Network Africa), a civil society platform focused on epidemic preparedness, community resilience, and health security advocacy across African countries.<\/p>\n\n\n\n
In conversation with Anthony Ombara, Senior Advisor, Kenya, we traced the what, why, and how of their journey with Apurva, from placing communities at the centre to closing the loop, and reimagining systems-level change across the countries they work in.<\/p>\n\n\n\n
One of RANA\u2019s areas of work revolves around bringing together lived experiences from focus group discussions, building an evidence-based systems-level transformation and a deeper understanding of resilience across Africa.<\/p>\n\n\n\n
Their \u2018what\u2019 was to go beyond summarising data and towards identifying patterns, contradictions, systems-level issues and advocacy priorities across multiple resilience themes. Preparedness and resilience are two important terms here. They are multidimensional, impacted by a community\u2019s physical, social, political, historical and financial facets. With multiple layers to gauge, Apurva helps to understand them, surface patterns, and identify gaps. This is very relevant to RANA\u2019s work, which greatly changes the way people live, enabling them to be more resilient.<\/p>\n\n\n\n
With Apurva LENS, they generated summaries faster, surfaced emerging themes and drafted analytical outputs across Kenya, Sierra Leone, South Africa and Uganda. They brought together knowledge sources in multiple languages like Afrikaana, English, Luganda, Sierra Leonean Krio, Swahili and Zulu.<\/p>\n\n\n\n
They dug deeper into the data collected using multiple prompts, examining it from different angles. They observed that LENS worked best when combined with human contextual judgement: AI helps to organise and accelerate analysis, whereas the human, backed with contextual knowledge, interprets. This resulted in advocacy products like policy briefs and talking points that resonated with various stakeholders: communities, governments and funders<\/p>\n\n\n\n