AmpAdvisor — home energy use on Alexa+

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for AmpAdvisor — home energy use on Alexa+

Inspiration

We noticed that many homeowners find energy consumption data complex and difficult to interpret de novo. We wanted to create a tool that transforms raw energy metrics into actionable advice using conversational AI. By focusing on Alexa+, we aimed to make energy management accessible and intuitive for everyone.

What it does

AmpAdvisor tracks home energy use and provides real-time analytics and personalized optimization strategies. Users can ask Alexa about their daily consumption patterns or receive suggestions on reducing their utility bills. The system integrates various energy sources to provide a holistic view of household efficiency.

How we built it

We developed the core logic using Python and deployed it via AWS Lambda for scalable execution. We utilized Amazon Bedrock for generative AI capabilities and the strands-agents-sdk to manage complex conversational flows. The interface is built for Amazon Alexa to ensure a seamless experience on Alexa+.

Challenges we ran into

Integrating disparate energy data formats into a cohesive model for the LLM required significant mapping efforts. We also struggled to ensure that the prompts in Amazon Bedrock remained concise while providing highly helpful advice. Tuning the latency between the Lambda functions and the Alexa response was a major technical hurdle.

Accomplishments that we're proud of

We are proud of how seamlessly the strands-agents-sdk handles complex queries regarding energy trends. The integration between Amazon Bedrock and Alexa+ resulted in a natural conversational experience. We successfully created a low-latency system that responds instantly to user inquiries.

What we learned

We learned much about the orchestration of agent-based workflows using the strands-agents-sdk. We also gained deep experience in how to structure energy-specific data for large language models. Understanding the nuances of the Alexa+ surface helped us design a more user-centric interface.

What's next for AmpAdvisor

We plan to expand our integrations with more smart appliances to automate energy-saving actions. We also want to add predictive modeling to help users forecast their monthly expenses. Future updates will include even more granular data visualization within the Alexa ecosystem.

Built With

• amazon-alexa
• amazon-bedrock
• ampadvisor
• aws-lambda
• energy
• strands-agents-sdk