Simple – AI food tracking
13 Jul, 2024
As design owner in the core team at Simple app, I helped turn food logging from a database-style tracker into the company’s strongest retention loop, designing AI noting, Nutrition Score, Food Feedback, and Avo Vision.
Timeline
2022–2024
Role
Senior Product Designer
Scope
UX/UI, Design System, Art direction
Product
0
1/N
, B2C, Wellness
Surfaces
iOS, Android
Simple is a health and weight loss app backed by Palta (Flo Health, Lensa, etc). It's one the leaders in weight-loss category with 15M+ downloads, $160M ARR, 323K MAU, and millions of users across the globe.
When I joined in 2022, the product was much narrower: a fasting timer with a clear habit loop and product-market fit, but limited surface area. The next growth question was simple: what comes after the fast?
The challenge
Food logging was the obvious next layer. It could anchor daily engagement, generate the personalization data the rest of the product needed, and build nutrition credibility that fasting alone couldn’t carry.
But Simple’s users weren’t calorie counters. They didn’t want MyFitnessPal-style admin. And strategically, Simple couldn’t win by copying the biggest calorie trackers. We had to make food logging useful enough to drive retention, but light enough to stay true to a product already built around simplicity.
Exploration
The food database was the bottleneck. UXR was blunt about it: users couldn’t find what they ate, search lacked autocorrect, and the act of “building a meal” reinforced the feeling that tracking was tedious and not for them.
I explored the system around it: daily progress, meal timelines, nutrition tips, post-log recommendations, lightweight feedback moments. Different shapes of the same question: could the loop become valuable enough that input friction stopped killing the habit?
Eighteen months of small, reactive experiments moved retention by only 1–2%. The exploration wasn’t a trophy wall. It was a team learning that incremental fixes to a database-first model weren’t going to be enough.
Effortless input
By early 2023, AI was good enough for a different bet. Instead of trying to build a better food database than MyFitnessPal, we decided to stop making users think like a database at all.
The new noting tracker I designed let people type or speak what they ate in normal language, then turned that into a structured entry the in-house feedback system could score. UXR landed where the design did: “Users perceived taking notes as easy and simple way of logging.” A sentence starter in the input field made the moment feel even lighter.
Noting tracker V1 shipped in Spring 2023 as the default flow on iOS and Android, including DE and FR. Between July 2022 and July 2023, total meal tracks grew 33% YoY, from 674k to 901k.
The honest read: the system metric moved, the habit metric didn’t quite compound. The experiment wasn’t conclusive on retention. We shipped it anyway because directionally it was the right reset, and because the deeper habit problem clearly needed more than a lighter keystroke.
Humane feedback
The noting tracker solved input. It didn’t solve the reward. After logging, users hit a wall of macros and nutrient bars: technically correct, practically useless.
UXR landed the point clearly: “When it comes to food you want to have a feeling of comfort.” People didn’t want to calculate. They wanted to know how the meal landed.
We took the European nutrition label system as a reference point and designed a four-tier meal score: Low, Fair, Good, Optimal. I worked with a graphic designer to develop flat illustrations that carried the tone the research pointed toward — warm, encouraging, non-shaming. It turned a nutrition result into something users were willing to look at.
The surface won the British Dietetic Association Digital Innovation Award 2023/24 for Simple App: Nutrition Scores and Food Feedback — external recognition, from a body that knows nutrition, that this was a genuinely useful approach.
Improving legibility
Once Food Feedback was in daily use, a new problem emerged. The character made nutrition feedback approachable, but it was taking space the nutrition result needed. Users coming back day after day were asking a more functional question: what did I score, why, and what do I do next? Specifics had to land in seconds, not just be implied by a colour.
The trigger was internal and behavioural: the character presentation made feedback harder to scan at speed and, for returning users, started to feel less reliable as a nutrition signal. The design task was clear: move the score to the top of the hierarchy. Result first. Reasoning underneath. Next action close. Personality steps back from the headline.
By this point I was also owning more of the AI Coach direction, and I brought that work into Feedback. AI-generated explanations replaced static copy, making the reasoning under the score more specific and useful. I collaborated with another designer on sharpening the four-tier scoring system into a more glanceable read.
The experiment validated the direction. D3 meal-track retention improved +3.6% for new users. Time on the result screen dropped 10%. Users spent less time on the result — not because they disengaged, but because they got the answer faster.
Multimodal tracker
By this point, the feedback loop worked. The remaining friction was the input itself — users still had to describe or type what they ate. Avo Vision removed that. Point the camera at a menu, a plate, or a dish, and the app does the rest.
We built a set of modes around the use cases users had been asking for for years. The app identifies the ingredients, scores the meal, and hands off to Coach Avo when you want to go deeper. Two taps from photo to logged meal.
I liked working on this enough to keep exploring in my own time. One idea I kept coming back to as AI models improved: collapse the separate scan modes into one, and surface specific nutrition feedback as you scan — not after, not in chat. We didn't ship this, but it was one of those moments where the work felt worth it on its own.
Avo Vision moved the product retention +2.1% and Avo retention 3d+ moved +4.5%, both significant. By May 2025, 15–20% of all daily meal logs came through the camera. MedTech Breakthrough named smart camera functionality as one of Avo's core pillars when Simple won Best Virtual Coach in 2025 and again in 2026.
Food logging had been an admin task. This is the version where it stopped feeling like one.
Conclusion
Tracker retention
+34%
Cumulative
Product retention
+12%
Cumulative
British Dietetic Association
Innovation Award
Food Score and Feedback
We turned food logging from a minor add-on to the fasting tracker into Simple’s strongest retention loop. It became the foundation for all features that followed.
My work moved metrics, won awards, and became part of Simple’s public story. Food tracking, Nutrition Scores, Avo Vision, and Simple’s broader AI nutrition direction were covered by Forbes, TODAY, TechCrunch, and featured on Apple’s App Store.
What I’m proud of is that we solved a constraint that once sounded almost impossible: making food logging genuinely useful without turning Simple into another calorie counter. We made it feel lighter, more human, and useful enough for people to keep coming back.
Acknowledgements
Built remotely with a team spread across the world: Elena Deshina, Alisa Korchazhnikova, Ivan Bakanovskiy, Val Scholz, Dimitri Nikogosov, Ro Huntriss, Josie Porter, Karina Delaine, Dmitrii Mochalov, Ruslan Dzhafarov, Alex Ovs, Hemank Sabharwal, David Johnston, and Alesia Privado.
Special thanks to Hemank and Alex. Working with you made this whole project genuinely fun.