Generative AI (GenAI) applications today often seem limited to frustratingly templated customer service chatbots or simple tools that crunch numbers and other information. We’re still in the early days of GenAI development, use, and adoption, so the technology is still developing to address pressing real-world use cases. But one thing is clear: With the right data at the right time, the possibilities for GenAI are limitless.
At the Forge the Future hackathon organized by Elastic and powered by Amazon Web Services (AWS) in Singapore, 12 teams gathered on February 28, 2025, to compete at creating advanced GenAI applications for use in industries including banking and financial services, manufacturing, healthcare, ecommerce, and the public sector.
Guided by experienced solutions architects, participants were equipped with a suite of tools that power the world’s leading companies. These tools included the Search AI Platform, which combines the precision of search and the intelligence of AI to build transformative applications, and Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies.
ElasticOn Singapore finals
Three teams — Buckle Up 4 AI, Singtel Code Crew, and SquareShift — were selected to participate in the finals held on March 4, 2025, during ElasticON Singapore at the Sands Expo and Convention Centre.
Their submissions were assessed by a panel of judges comprising:
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Shay Banon, Founder and CTO, Elastic
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Hock Chuan Lim, Deputy Director, Product Management, AI Products, xDigital, HTX (Home Team Science & Technology Agency)
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Laurence Liew, Director AI Innovation, AI Singapore
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Anil Nallamotu, Head of Global Strategic Partners Solution Architecture, APJ, AWS
The winning team, Buckle Up 4 AI from Standard Chartered Bank, presented an application that transforms financial data into actionable insights. Using Elastic’s recent earnings call transcripts and publicly available data, Team Buckle Up 4 AI showcased a demo of their generative AI solution replying to queries about Elastic’s financial performance.
“Seeing the other teams’ ideas has been enlightening, especially since they cover solutions for various industries like healthcare and fashion,” said Karthikeyan GJ, a winning team member. “Being able to showcase our ideas to the judges and ultimately win caps off a very positive and eye-opening experience for us.”
When asked about the hackathon and the solutions presented, Laurence Liew opined, “The use cases presented were really interesting and of high quality. I really liked Buckle Up 4 AI’s solution as they demonstrated how organizations can make AI work for you and how it can be used in an innovative way.”
Hock Chuan Lim added, “I found it very impressive that all the participants were able to produce innovative use cases within the three-hour limit. One of the solutions that caught my interest was the one that ingests data from health devices and wearables to generate personalized and actionable insights. I pay more attention to my health as I get older, and I found the use case to be a compelling one.”
Powering AI with search
GenAI will remain a prominent topic in Singapore as organizations transition from pilot projects to real-world technology applications. The country has expressed its goal to become a Smart Nation and lead innovation in AI. In the 2025 budget, the government committed up to S$150 million for a new Enterprise Compute Initiative to help companies adopt artificial intelligence and to provide access to artificial intelligence tools and computing power. There will also be a S$3 billion top-up to the National Productivity Fund that is expected to help Singapore compete in new frontier areas, including artificial intelligence and quantum computing.
As innovation continues, search will be essential for powering GenAI with accurate, relevant data at the right time. Advanced search capabilities like vector search can deliver results based on the underlying meaning of the query, which can include images, video, audio, or text instead of keywords. Other techniques like retrieval augmented generation (RAG) enable organizations to search proprietary data sources and provide context that grounds general output from large language models (LLMs) for more accurate, real-time responses. With LLMs being AI models trained on massive amounts of data to enable them to understand and generate natural language responses, RAG enhances the accuracy and reliability of such models with information retrieved from relevant data sources that are outside their training data sources.
When equipped with these search capabilities, organizations can make full use of their proprietary, highly contextual data, unlocking GenAI’s potential.
Learn more about how AI fuels innovation at Elastic.
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