Overview
Problem
One of problems we found in the modern calories tracker app is that they normally require users to
manually input the names of foods they consume (across multiple categories)
rely on calorie data entered by other users which may be inaccurate due to individual differences in portion size
Urban users tend to abandon tracking because logging is slow, repetitive, and cognitively exhausting.
Drop-off caused by effort, not lack of intent
👉 an opportunity for AI intervention
Solution
Target User: Urban female professionals pursuing fat loss via diet control.
Conclusion
Effortless calorie tracking, powered by human-centered AI
Calorie Camera demonstrates how thoughtful design and AI integration can transform a traditionally tedious task — calorie tracking — into an effortless, user-centric experience. By identifying core pain points in existing solutions (manual entry, high interaction cost, and inaccurate data), we crafted an MVP that empowers users to log meals with a single photo while maintaining transparency, control, and flexibility in their decision-making. In just four weeks, the project validated a rapid, AI-augmented workflow and shipped an AI-powered product that achieved positive early user reception and supported personalized nutritional insights. This case underscores how combining human-centered design principles with generative AI can significantly reduce friction, meet real user needs, and open new avenues for data-driven personalization in health tech.
AI Lab
Big thanks to my team — I truly appreciated the opportunity to work on this product together.
