The Challenge
Like many organisations investing in Generative AI, they had promising use cases and a team capable of building models, but needed greater confidence in how those solutions could be successfully deployed, managed and governed in a production environment.
The team lacked experience in operationalising and deploying GenAI solutions at scale within Databricks and was looking for guidance around best practices, hosting, deployment and overall production readiness.
Key challenges included:
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Understanding whether existing AI use cases were ready for production
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Identifying gaps that could prevent successful deployment
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Establishing best practices for hosting and deploying AI solutions within Databricks
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Understanding how to operationalise LLMs and AI agents
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Building the internal knowledge and confidence needed to take solutions beyond experimentation
The Solution
Advancing Analytics delivered a Bronze-tier LLMOps advisory engagement focused on assessing the insurer's current AI initiatives and creating a clear route towards production.
As part of the engagement, the team:
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Reviewed current AI initiatives to understand their existing approach and production-readiness requirements
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Evaluated two key use cases against multiple production-readiness dimensions
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Delivered practical coaching and knowledge-sharing sessions around Databricks capabilities and best practices
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Provided guidance on operationalising LLMs and AI agents, including deployment, hosting and operational practices
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Identified production-readiness gaps that needed to be addressed before deployment
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Created a structured readiness assessment and roadmap to help move solutions towards production scale and supportability
Advancing Analytics brought specialist expertise in Databricks AI capabilities, LLMOps and productionisation best practices, allowing the guidance to be practical and specific to the platform they were already using.
Why Advancing Analytics?
Advancing Analytics was selected through the Databricks partner ecosystem and brought deep expertise in Databricks AI capabilities, LLMOps and productionisation best practices. As a specialist Databricks partner, the team was able to provide practical, platform-specific guidance.
The Results
The engagement gave the organisation a clearer understanding of what was needed to move its GenAI initiatives from experimentation towards production, alongside a prioritised roadmap for addressing the gaps identified.
Key outcomes included:
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A clear, prioritised roadmap towards production deployment
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Greater confidence in their AI strategy and understanding of production requirements
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Reduced uncertainty around deployment strategy, including hosting and operational considerations
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Improved internal capability and knowledge of Databricks and LLMOps best practices
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Clear prioritisation of next steps for the AI use cases assessed
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A more structured approach for moving AI solutions from experimentation towards production and supportability
