Inspiration
For 1 million people to make good reproductive and fertility health at work available for 1 million men and women in the UK
What it does
It allows employees to get answers to questions they have regarding their reproductive and fertility health in a confidential way
How we built it
Python back-end using Google AI Agent ADK , Streamlit front-end, Pinecone Vector store, Supabase Managed SQL DB
Challenges we ran into
Achieving memory, persistent and personalised storage of chats. Inconsistent formatting of output and citations by the agent eg it cited the internal documents differently from those obtained externally
Accomplishments that we're proud of
Making it work! Integrating 3 different models - 2 Gemini models and 1 Open AI model into our solution using Open Router and Lite LLM
What we learned
Lots about the Google ADK which we're looking to leverage on other projects, multi-package implementation in Python, the uv package manager, Managed Cloud DBs
What's next for Policy Pulse
We will build out the solution to be more robust, more accurate and to give a better user experience. We will have extensive evals
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