AIFlow

AWS Quicksight Dashboard
AWS Quicksight Analysis
Project Workflow

Inspiration

Product managers don’t have real time insights into product performance and often rely on metrics such as app store reviews, problem tickets, and user activity logs to understand product performance. We wanted to show how agentic AI could turn scattered analytics into clear and actionable insights.

What it does

AIFlow is an agentic analytics microservice that aggregates product usage, engagement, and feedback data, then uses Bedrock LLM to identify churn risks, underperforming workflows, and high-impact product improvements through dashboards and natural-language queries.

How we built it

We built AIFlow using AWS serverless services. Lambda handles data ingestion and processing, S3 stores raw and aggregated analytics, Bedrock analyzes trends and generates recommendations, and QuickSight visualizes KPIs and insights.

Challenges we ran into

Designing meaningful product metrics, reasoning across multiple data sources, and translating raw analytics into actionable recommendations were key challenges.

Accomplishments that we're proud of

We built an end-to-end agentic system that demonstrates measurable business impact, and shows how LLMs can support real product decision-making.

What we learned

We learned how agentic AI can take analytics to aid strategy, and how cloud-native architectures make it possible to build scalable, insight-driven product tooling.