Using software tools and AI to audit e-commerce food regulations
summary
Prototype scrapes product pages, runs automated tests and applies NLP to extract ingredient lists, check allergen styling (uppercase/bold) and detect missing ingredients or technical errors, then surfaces 2–3 suspicious products for manual review. Implementation uses web scraping, traditional test automation and a simple chatbot interface that calls AI classifiers to label ingredients and suggest fixes; it also generates marketing rewrites and can monitor competitor prices. The prototype runs end-to-end and successfully flags compliance issues on real e‑shop pages.
AI-written summary · about
chapters (10)
- 0:00Introduction
- 0:23About our startup
- 1:27Food regulation challenges
- 3:14Key compliance requirements
- 5:11Allergen labeling rules
- 8:09Automated compliance checking
- 10:17AI-powered chatbot demo
- 12:48Additional e-commerce tools
- 13:30Benefits of automation
- 14:13Q&A
related talks
43mAI w/o Spaghetti: Clean Architecture in the Age of AI
23mConnecting Food Journeys through AI and the Open Food Platform
48mAI Solutions for Modern Digital Marketing
55mAI-First Testing
20mJavascript AI Cookbook: Building AI powered product features
27mBuilding AI applications: Successes and Lessons
01h 24mStay Ahead in AI with Product Engineering and AI Native Tools
22mAPI Product Management with an AI tools & mindset