{"id":17313,"date":"2025-12-09T11:54:33","date_gmt":"2025-12-09T11:54:33","guid":{"rendered":"https:\/\/teamkinetic.co.uk\/blog\/?p=17313"},"modified":"2025-12-09T13:53:29","modified_gmt":"2025-12-09T13:53:29","slug":"conversation-triumphs-at-the-odi-volunteering-hackathon","status":"publish","type":"post","link":"https:\/\/teamkinetic.co.uk\/blog\/2025\/12\/conversation-triumphs-at-the-odi-volunteering-hackathon\/","title":{"rendered":"Conversation Triumphs at the ODI Volunteering Hackathon"},"content":{"rendered":"\n

What an incredible event. On behalf of the entire winning team, ‘Table 1,’ I want to start by extending our sincere thanks to the Open Data Institute organisers for arranging the Volunteering Hackathon 2025! The atmosphere was lively and fun, and it was genuinely inspiring to see so many dedicated people tackling the challenges of open data infrastructure and improving volunteer systems.\u00a0<\/p>\n\n\n\n

This victory, and all the outcomes from the day, are a testament to the fact that when passion meets technology, real community impact is indeed inevitable.<\/p>\n\n\n\n

I am immensely proud of our team: Chris Martin<\/strong>, Dr. Amy Burnett<\/strong>, Andrew Mene-Otubu<\/strong>, Matt Parker<\/strong>, Murphy Campbell<\/strong>, Aaron Amato<\/strong>, and Nyaha Duri<\/strong>. To my teammates – thank you. Your collective brilliance, from deep domain expertise to sharp technical skills, made our prototype, a reality in such a short time.<\/p>\n\n\n\n

The Challenge: Making Volunteering Human-Centric<\/strong><\/h3>\n\n\n\n

We focused on user-centric discovery and experience, and specifically tackled the question: “Can finding a volunteer role be as easy as asking a friend?<\/strong><\/em>” <\/p>\n\n\n\n

We aimed to create a conversational, generative search agent that leverages standardised open data to move beyond simple keyword filtering, making it effortlessly accessible.<\/p>\n\n\n\n

The truth is, traditional search forces people to translate their human needs into rigid database queries. We saw an opportunity to build a system that understands the nuance of intent. Our solution, Project Alpha, is a natural language interface for volunteer opportunities that delivers a “Humanistic Search for Humans”.<\/p>\n\n\n\n

Building the Brain: A Training Context for the LLM<\/strong><\/h3>\n\n\n\n

Our core innovation was not just using a Large Language Model (LLM) – we worked on linking the open dataset via LLM to the Model Context Protocol (MCP) – but also how we trained it. We knew that to achieve truly empathetic and accurate matching, our model couldn’t be a generic bot; it needed a soul.<\/p>\n\n\n\n

This is where the groundbreaking research of our own Dr. Amy Burnett<\/strong> (and Catherine Wilder) was instrumental. We adapted their framework, developed under a British Academy Policy Innovation Fellowship, to create a deep training context for our AI agent. This framework moves beyond simple transactional data like “skills” and “location” to probe the user’s purpose and story.<\/p>\n\n\n\n

The LLM\u2019s training wasn’t just about indexing 10,000+ opportunities; it was about internalising human motivation. The key questions we built into the agent’s context included:<\/p>\n\n\n\n