FluxCareAI

Software architecture

Inspiration

Consumers are expected to make healthy and safe choices, yet the information needed to do so is often scattered, technical, or hidden in regulatory databases, lawsuits, and long reports. Most people don’t have the time or expertise to interpret FDA violations, recalls, or media investigations before buying or consuming a product. We wanted to make product transparency instant and accessible, allowing anyone to quickly understand whether a product is safe, trustworthy, and healthy.

What it does

FluxCareAI lets users take a photo of a consumer product and receive a clear, evidence-backed safety and trust report about the product and the company behind it. The app identifies the product, resolves the correct manufacturer, and analyzes lawsuits, FDA warnings, recalls, and documented safety concerns. Results are presented in a client-friendly format explaining what was found, what the risks are, and gives recommendations to the client.

How we built it

We built FluxCareAI using Expo with React Native for the mobile app and FastAPI for the backend. The frontend handles image capture and report display, while the backend coordinates a multi-agent AI pipeline. After a photo is submitted, AI agents identify the product, resolve the correct company, gather evidence from the FDA, lawsuits, and news sources, and synthesize the findings into a client-friendly report.

Challenges we ran into

A major challenge was defining the right context and scope for each AI agent. Overly broad research instructions caused agents to take on too much work, which could disrupt the report-generation process. We solved this by clearly constraining each agent’s role, improving reliability and performance.

Accomplishments that we're proud of

We are proud to build a fully functional end-to-end system that turns a simple product photo into a detailed, evidence-backed safety report. We’re especially proud of creating a multi-agent AI pipeline that reliably gathers regulatory and legal information while enforcing credibility and transparency before reaching the user.

What we learned

We learned the importance of clear task definition and scoped responsibilities when working with multiple AI agents. We also gained experience translating complex regulatory and legal data into information that is clear, accurate, and usable for everyday consumers.

What's next for FluxCareAI

With sufficient funding, we plan to evolve FluxCareAI into a fully released product or startup. Next steps include scaling the infrastructure, expanding coverage across more product categories, improving real-time data access, and refining the user experience to make FluxCare a trusted consumer safety tool.