Mandai Wildlife Group has launched Animal 360, a secure AI-powered platform that consolidates more than 600,000 animal care records into a single searchable system for its veterinarians and keepers across five Singapore attractions. The platform, built with implementation partner Infinite Lambda on Snowflake Cortex AI and large language models, lets life sciences teams query decades of animal history using natural language instead of manually pulling files from separate systems.
The group oversees 20,000 animals across more than 900 species at Singapore Zoo, Night Safari, River Wonders, Bird Paradise, and Rainforest Wild Adventure. Each animal accumulates extensive logs over time - keeper observations, veterinary treatments, breeding records, and nutrition notes - that were previously siloed across husbandry, veterinary, nutrition, and genealogy systems.
Faster answers for clinical decisions
Animal 360 unifies those records into one interface, summarising data in minutes so staff can build operational context before applying professional judgement. The platform is already producing results in practice: primate keepers at Singapore Zoo used it to analyse historical mating records within a troop of proboscis monkeys, improving due date estimations for pregnant females.
The system is entering wider deployment after a pilot with Life Sciences teams, with refinements ongoing based on staff feedback. Future use cases include scaling welfare and pathology data analysis to detect health patterns more efficiently than manual reviews allow. The group has also developed a parallel platform, Business 360, to support its commercial operations.
For operations professionals, the underlying pattern here is straightforward: when data that already exists gets unified and made queryable, routine information-gathering work shrinks dramatically. The same approach applies across industries - the bottleneck is rarely missing data, but the time spent finding and synthesising it. The team has also developed a parallel platform for commercial operations, suggesting the model extends beyond animal care into broader business functions. Those exploring similar consolidations can examine how Generative AI and LLM capabilities enable natural-language querying of institutional knowledge, or review practical AI for Operations applications in comparable settings.
What the team says
Siew Yeow Loye, chief information officer at Mandai Wildlife Group, described the shift in practical terms: "Our vets and keepers have always drawn on multiple sources to understand an animal's history, but bringing that information together took time. With Animal 360, they can simply ask a question and have it pulled together for them, so they can spend less time gathering information and more time doing what they do best - caring for the animals."
Jenny Koh, country manager at Snowflake Singapore, noted that "AI is only as good as the data behind it, and what Mandai Wildlife Group has built shows what becomes possible when that data is unified, governed and accessible." Nas Radev, CEO of Infinite Lambda, added: "What we built with the Mandai Wildlife Group gives their teams the tools to act on data they have always had but never been able to fully use."
Why this matters for operations
The lesson for operations teams is concrete: if your organisation holds years of records across separate systems, the first step isn't adopting AI - it's deciding which data deserves unification. Mandai's approach worked because the group had a clear operational question (how do we give carers faster access to animal history) and built the platform around that question, not around the technology. Operations leaders evaluating similar projects should start by identifying the single most time-consuming manual data pull in their workflow, then test whether natural-language querying of that data saves enough time to justify the integration effort.
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