The City of Melbourne has selected Databricks to build a unified data and AI platform that now supports more than 700 production datasets and over 40 AI use cases across the council.
The move expands the council's Data Central platform, which brings together information previously spread across separate tools, systems and on-premises infrastructure. A review of the technology landscape found staff were spending large amounts of time gathering and combining data manually, while inconsistent standards and fragmented systems slowed analysis and collaboration.
One of the most visible applications is a pedestrian chatbot that lets council staff, city planners and local businesses ask plain-language questions about foot traffic patterns across Melbourne. Drawing on near real-time information from the city's sensor network, the tool is intended to reduce the need for technical data requests.
According to the city, the chatbot can be used to examine movement trends in different parts of Melbourne and support decisions on precinct planning, staffing and local activity. The wider platform is also being used for projects including a knowledge bank, predictive city operations, citizen service intelligence and financial management.
Platform shift
The council consolidated onto Databricks after identifying an opportunity to modernise how data was managed and used internally. That meant moving away from what it described as a legacy mix of tools and siloed systems to a single platform for data, analytics and AI.
For the City of Melbourne, the project reflects a broader effort by public bodies to make operational data easier to access across teams while putting more AI applications into day-to-day use. The shared foundation has enabled the council to put hundreds of datasets into production and support dozens of use cases, rather than treating AI projects as isolated pilots.
The pedestrian chatbot is aimed at both internal and external users. Local businesses can use it to understand how people move through the city and apply those insights to decisions such as staffing levels, trading hours and local activation. Council teams can use the same information for planning and economic analysis.
The city may later add other data sources to the chatbot, including information on events, disruptions and road traffic, to provide a broader view of activity across Melbourne. This would extend the system beyond pedestrian counts and link movement data with other indicators that shape how residents, workers and visitors use the city.
Councillor Andrew Rowse, Innovation and Education Portfolio, City of Melbourne, outlined the council's approach. "The partnership with Databricks focuses on creating AI solutions designed to solve real-world challenges while building internal capability in enterprise AI - aligned with CoM's strategic priorities and AI principles. Ultimately, every solution we build is in service of the Melbourne community, and the foundation we've built with Databricks gives us the confidence to scale that impact across the city," Rowse said.
Databricks views the Melbourne project as part of a wider pattern in government and public services, where organisations are seeking to move AI projects into operational settings with governance over underlying data. It pointed to the combination of a centralised data environment and practical service applications as the model emerging in the sector.
Adam Beavis, Vice President and Country Manager, Databricks ANZ, said the shift is moving beyond experimentation. "The best AI fades into the experience, helping organizations move faster, make better decisions, and deliver stronger outcomes. That's what the City of Melbourne is building with Databricks: a secure, governed foundation for data and AI th at powers more responsive city services, smarter operations, and real impact for residents. CoM is part of a broader shift we're seeing across the public sector as organizations move AI from experimentation into production at scale," Beavis said.