In partnership with RMIT University, this active ARC Linkage project develops a federated fine-tuning framework for secure, privacy-preserving Generative AI models using distributed data, with practical applications across healthcare, energy, and finance.
Serendib Systems is listed as a successful Stage 1 feasibility recipient under the BRII Australian Cyber Security Strategy Challenge, supporting early validation and development of practical cyber-focused innovation outcomes.
This RMIT University collaboration under ARC Linkage LP240100417 focuses on privacy-preserving federated fine-tuning for GenAI models, enabling secure, distributed AI innovation and practical deployment pathways for industry.
We deliver applied AI and innovation programs that combine product discovery, technical feasibility, and production-ready engineering.
From proof-of-concepts to scaled deployment, we support your roadmap with rapid experimentation, measurable outcomes, and resilient implementation practices.
Validate concepts quickly with structured prototypes and measurable success criteria.
Prioritize implementation based on technical feasibility, impact, and delivery risk.
Bridge experimentation and production with robust engineering, QA, and deployment practices.
Support engagement models that fit grant, research, and industry partnership pathways.