SafeBite AI

Transparent Risk Intelligence to Keep Food Safe, Every Time.

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Inspiration

We noticed that traditional food safety systems rely heavily on average scoring, which can hide critical risks. A product containing a single banned additive may still appear “safe,” putting consumers at risk. This inspired us to create SAFEBITE AI, a transparent and explainable system that ensures no dangerous additive goes unnoticed.

What it does

SAFEBITE AI scores products based on additive risk values, classifies them as Low, Medium, or High risk, and automatically triggers a safety override for banned additives. It provides transparent, auditable insights through a dashboard, helping regulators make informed decisions and protect public health.

How we built it

We designed a MySQL relational database to store products, additives, and risk scores. Using SQL-based rules, we calculate average additive risk and apply overrides for banned substances. The dashboard is built with HTML, CSS, and Chart.js, showing summary cards, pie charts, and a High Risk products table, all fetching data via PHP endpoints.

Challenges we ran into

  • Ensuring the system remains explainable while handling multiple risk rules.
  • Aggregating real-time data efficiently from the database.
  • Designing a user-friendly dashboard that is both informative and visually clear.
  • Balancing technical correctness with regulatory transparency.

Accomplishments that we're proud of

  • Built a fully functional dashboard showing risk distribution and High Risk products.
  • Implemented rule-based overrides to prevent dangerous additives from being hidden.
  • Created an auditable, explainable system suitable for real-world compliance.

What we learned

  • Explainable AI is critical for trust in regulatory contexts.
  • SQL-based logic can handle complex risk scoring without machine learning.
  • Dashboard design matters for effective visualization of compliance data.

What's next for SAFEBITE AI

  • Integrate real-time API feeds for additive monitoring.
  • Add IoT sensors and anomaly detection for proactive risk alerts.
  • Implement searchable, sortable tables and interactive charts for the dashboard.
  • Explore blockchain verification for end-to-end supply chain transparency.

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