2025-04-24 –, Palladium
Ever wondered how to use GenAI to enable self-service analytics through prompting? In this talk, I will share my experience of building a multi-tenant conversational analytics set-up that is built into a Software-as-a-Service (SaaS) platform. This talk is intended for AI engineers, data scientists, software engineers and anyone interested in using GenAI to power conversational analytics using open-source tools.
I will discuss the challenges faced in designing and implementing, as well as the lessons learned along the way. We'll answer questions such as, why offer analytics through prompting? Why multi-tenancy and makes it so difficult? How to build it into an existing product? What makes open-source the preferred choice over proprietary solutions? What could the implications be for the analytics field?
This talk will start by answering the question: What is conversational analytics and how does it work? After which we'll dive into why this was built and how the implementation was done.
- How analytics in SaaS can be fundamentally improved by conversational analytics (5 mins).
- How the Text-to-SQL fundament was shaped using RAG with Embeddings in PGVector (5 mins).
- Dealing with multi-tenancy in PostgreSQL and BigQuery to ensure data segregation & security (5 mins).
- How to handle tenant specific pre-training and training examples (5 mins).
- Building this into an existing application and supporting integrations (5 minutes).
- Conclusion and thoughts on the implications for the field of analytics (5 mins).
In the end you should have a good idea on why conversational analytics can be a game changer, what the pitfalls are and how to build it with open source technologies.
Intermediate
Expected audience expertise: Python:Novice
I speak & write about my experiences in the world of data & AI. This comes from the perspective of having worked across data science, data engineering and ML engineering in start-ups, scale-ups and enterprises.