Should I Build It Should I Build It
05 May 2025
Artificial Intelligence Cooking Health & Fitness

Food app that uses AI to detect ingredients and recommend recipes ...

...while doing lightweight calorie tracking on the side

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

Your idea for a food app using AI to detect ingredients, recommend recipes, and track calories puts you in a crowded space. We found 26 similar products, which indicates high competition. The overall engagement in this category is low, with an average of only 3 comments per product. Given the number of existing solutions and the lukewarm engagement, you're entering what we call a 'Swamp' category. This means several similar solutions already exist, but none have truly broken through to widespread popularity. To succeed, you'll need a truly novel approach or a laser focus on an underserved niche.

Recommendations

  1. Deeply analyze why existing food and calorie tracking apps haven't achieved widespread, enthusiastic adoption. Are they too cumbersome? Inaccurate? Socially awkward? Look at the discussions and criticisms for similar products. For example, many users criticize existing calorie tracking apps for privacy issues related to voice input, inaccurate calorie estimations, and difficulty tracking sugar and oil content.
  2. If you're set on pursuing this idea, identify a very specific niche that current apps are failing to serve. Don't try to be everything to everyone. A successful niche could be people with specific dietary restrictions, or those who prefer a completely hands-off, image-based approach. Consider the feedback from MealByMeal launch; users requested more customization options, including manual input/output adjustments and support for custom foods and diverse measurements.
  3. Explore creating tools or integrations for existing nutrition and recipe platforms instead of building a standalone app. This could involve developing an AI-powered ingredient recognition API that other apps can use, or create a recipe recommendation plugin for popular meal planning services.
  4. Pivot to an adjacent problem that might be more promising. For example, instead of calorie tracking, focus on AI-powered meal planning for families with dietary restrictions, or a tool that helps restaurants optimize their menus based on ingredient costs and customer preferences.
  5. Prioritize accuracy and usability. Many users complain about inaccurate calorie estimations. Focus on refining your AI's ability to precisely identify ingredients and estimate nutritional content. Also, consider user feedback on privacy; offer text-based input options alongside image-based tracking.
  6. Carefully consider your business model. Many existing apps rely on subscriptions, which can be a barrier to entry. Explore alternative monetization strategies like partnerships with food brands, or offering premium features for a one-time fee.
  7. Focus on a seamless user experience, especially around image recognition and calorie input. As noted in discussions around similar products, a frictionless user experience is key to sustainable, hassle-free food tracking.

Questions

  1. What unique dataset will your AI be trained on to ensure superior ingredient recognition and calorie estimation compared to existing solutions, and how will you continuously update it to maintain accuracy?
  2. How will your app address the potential for triggering or reinforcing eating disorders associated with calorie counting, as raised by users in the MealByMeal launch feedback?
  3. What specific privacy-preserving measures will you implement to address user concerns about data security and usage, particularly with image and voice input, considering the criticisms of similar apps?

Your are here

Your idea for a food app using AI to detect ingredients, recommend recipes, and track calories puts you in a crowded space. We found 26 similar products, which indicates high competition. The overall engagement in this category is low, with an average of only 3 comments per product. Given the number of existing solutions and the lukewarm engagement, you're entering what we call a 'Swamp' category. This means several similar solutions already exist, but none have truly broken through to widespread popularity. To succeed, you'll need a truly novel approach or a laser focus on an underserved niche.

Recommendations

  1. Deeply analyze why existing food and calorie tracking apps haven't achieved widespread, enthusiastic adoption. Are they too cumbersome? Inaccurate? Socially awkward? Look at the discussions and criticisms for similar products. For example, many users criticize existing calorie tracking apps for privacy issues related to voice input, inaccurate calorie estimations, and difficulty tracking sugar and oil content.
  2. If you're set on pursuing this idea, identify a very specific niche that current apps are failing to serve. Don't try to be everything to everyone. A successful niche could be people with specific dietary restrictions, or those who prefer a completely hands-off, image-based approach. Consider the feedback from MealByMeal launch; users requested more customization options, including manual input/output adjustments and support for custom foods and diverse measurements.
  3. Explore creating tools or integrations for existing nutrition and recipe platforms instead of building a standalone app. This could involve developing an AI-powered ingredient recognition API that other apps can use, or create a recipe recommendation plugin for popular meal planning services.
  4. Pivot to an adjacent problem that might be more promising. For example, instead of calorie tracking, focus on AI-powered meal planning for families with dietary restrictions, or a tool that helps restaurants optimize their menus based on ingredient costs and customer preferences.
  5. Prioritize accuracy and usability. Many users complain about inaccurate calorie estimations. Focus on refining your AI's ability to precisely identify ingredients and estimate nutritional content. Also, consider user feedback on privacy; offer text-based input options alongside image-based tracking.
  6. Carefully consider your business model. Many existing apps rely on subscriptions, which can be a barrier to entry. Explore alternative monetization strategies like partnerships with food brands, or offering premium features for a one-time fee.
  7. Focus on a seamless user experience, especially around image recognition and calorie input. As noted in discussions around similar products, a frictionless user experience is key to sustainable, hassle-free food tracking.

Questions

  1. What unique dataset will your AI be trained on to ensure superior ingredient recognition and calorie estimation compared to existing solutions, and how will you continuously update it to maintain accuracy?
  2. How will your app address the potential for triggering or reinforcing eating disorders associated with calorie counting, as raised by users in the MealByMeal launch feedback?
  3. What specific privacy-preserving measures will you implement to address user concerns about data security and usage, particularly with image and voice input, considering the criticisms of similar apps?

  • Confidence: High
    • Number of similar products: 26
  • Engagement: Low
    • Average number of comments: 3
  • Net use signal: 20.2%
    • Positive use signal: 22.9%
    • Negative use signal: 2.7%
  • Net buy signal: -0.9%
    • Positive buy signal: 1.0%
    • Negative buy signal: 1.8%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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