Foodify

At INVO, I owned AI product work for Foodify — from choosing use cases that could genuinely help users through feature behavior, delivery, QA, and post-release iteration. I combined product decisions with hands-on delivery, working with product, UX, mobile, frontend, backend, and business teams.

With a broad meal selection and different dietary preferences, another set of filters is not enough. The challenge was to shorten the path from a user’s needs and constraints to a relevant choice and order.

AI should help at the exact moment of decision — understand preferences, make the right meals easier to discover, and support ordering. Not as a standalone demo, but as part of the core product flow.

• Led AI initiatives from discovery and prioritization through behavior definition, release, and iteration.
• Delivered a customer-facing AI assistant supporting meal discovery, personalization, selection, and ordering.
• Developed Python-based recommendation and personalization systems around customer preferences and dietary needs.
• Defined UX, acceptance criteria, validation, and QA for AI features.
• Coordinated delivery with product, UX, mobile, frontend, backend, and business teams.

AI was not a standalone experiment or a marketing label. It became part of the customer journey: from meal discovery and recommendations to personalization and user support through an assistant. I remained responsible for both product decisions and the quality of feature behavior.

At a public level, I can describe LLM applications plus Python-based recommendation and personalization systems integrated into an existing mobile and web product. Internal architecture, data flows, evaluations, performance, and commercial data remain confidential.

• Prioritize AI use cases according to user and product value.
• Design AI as part of existing product flows, not as a standalone demo.
• Connect recommendations with customer preferences and dietary needs.
• Define behavior, quality criteria, and QA before release.

The AI features shipped inside a publicly available foodtech platform across mobile and web. It is my strongest example of taking Applied AI from a problem and product decisions to release.