Should I Build It Should I Build It
07 May 2025
Artificial Intelligence Cooking Food & Drink

a food calory ai app track how much calory in a capture food

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

You're entering a crowded space: AI-powered calorie tracking apps. Our data shows 24 similar products already exist, which means there's established demand, but also stiff competition. Engagement with these existing apps is low, averaging only 1 comment per launch. We don't have strong 'use' or 'buy' signals from the comments, suggesting that while people are curious, they're not necessarily committed to using or paying for these types of apps. The 'Swamp' category description fits well: many have tried, but few have truly broken through with something that users love. It looks like the general consensus from early adopters is that these apps are great ideas, but not perfect yet.

Recommendations

  1. Given the 'Swamp' category, thoroughly research why existing calorie tracking apps haven't achieved widespread success. What are the pain points users consistently complain about? Look closely at reviews, app store comments, and user feedback on competitor websites. A recurring theme in similar product launches revolves around accuracy and the lack of detailed nutritional information, specifically macronutrient breakdowns. Address those shortcomings in your app.
  2. If you decide to proceed, identify a specific niche or underserved group within the broader market of calorie counters. For example, could you focus on users with specific dietary restrictions (vegan, gluten-free), athletes tracking performance, or individuals managing specific health conditions like diabetes? Specialization can provide a competitive edge. If you focus on a specific group, create content that is targeted to this group's needs. Create example meal plans, sample workouts, and lifestyle adjustments to help your core users.
  3. Consider if you could create features for the existing app providers instead of building a new app. Are there opportunities to offer AI-powered calorie estimation as an API or SDK that existing apps can integrate? This approach reduces your go-to-market risk and allows you to capitalize on the existing user base of established players. Another idea could be to build a database that merges all of the food data that is collected from the different apps.
  4. Explore adjacent problems in the health and wellness space. Perhaps there's a greater need for AI-powered meal planning, automated grocery shopping based on dietary needs, or personalized workout recommendations. Broadening your scope might uncover a more promising opportunity with less direct competition. One of the biggest adjacent opportunities is AI-powered recipe generation.
  5. Before investing significant time and resources, conduct a pilot test with a small group of target users. This will give you invaluable feedback on your app's accuracy, usability, and overall value proposition. Based on user feedback from similar products, accuracy in calorie estimations, especially for home-cooked meals and dishes with mixed ingredients, is crucial. Prioritize this during your pilot test.
  6. Create content to grow awareness for the benefits of your product. Calorie tracking apps often face an uphill battle to gain users. Content can show how your app improves your customers' lives. Make sure to include real-world examples that your users can relate to. Ensure you tailor the content for your specific customer segment.

Questions

  1. Given the existing solutions in the market, what is your plan to ensure the AI in your app provides calorie estimations with significantly higher accuracy, especially for complex meals, compared to current offerings?
  2. Considering the low engagement observed in similar calorie tracking apps, how will you create a user experience that is not only accurate but also genuinely engaging and motivates users to consistently track their food intake over the long term?
  3. Many users are concerned about the sugar and oil content of their food. Given the user's feedback on similar products, what is your plan to provide users with a more comprehensive nutritional breakdown, including macronutrients, sugar, and oil content?

Your are here

You're entering a crowded space: AI-powered calorie tracking apps. Our data shows 24 similar products already exist, which means there's established demand, but also stiff competition. Engagement with these existing apps is low, averaging only 1 comment per launch. We don't have strong 'use' or 'buy' signals from the comments, suggesting that while people are curious, they're not necessarily committed to using or paying for these types of apps. The 'Swamp' category description fits well: many have tried, but few have truly broken through with something that users love. It looks like the general consensus from early adopters is that these apps are great ideas, but not perfect yet.

Recommendations

  1. Given the 'Swamp' category, thoroughly research why existing calorie tracking apps haven't achieved widespread success. What are the pain points users consistently complain about? Look closely at reviews, app store comments, and user feedback on competitor websites. A recurring theme in similar product launches revolves around accuracy and the lack of detailed nutritional information, specifically macronutrient breakdowns. Address those shortcomings in your app.
  2. If you decide to proceed, identify a specific niche or underserved group within the broader market of calorie counters. For example, could you focus on users with specific dietary restrictions (vegan, gluten-free), athletes tracking performance, or individuals managing specific health conditions like diabetes? Specialization can provide a competitive edge. If you focus on a specific group, create content that is targeted to this group's needs. Create example meal plans, sample workouts, and lifestyle adjustments to help your core users.
  3. Consider if you could create features for the existing app providers instead of building a new app. Are there opportunities to offer AI-powered calorie estimation as an API or SDK that existing apps can integrate? This approach reduces your go-to-market risk and allows you to capitalize on the existing user base of established players. Another idea could be to build a database that merges all of the food data that is collected from the different apps.
  4. Explore adjacent problems in the health and wellness space. Perhaps there's a greater need for AI-powered meal planning, automated grocery shopping based on dietary needs, or personalized workout recommendations. Broadening your scope might uncover a more promising opportunity with less direct competition. One of the biggest adjacent opportunities is AI-powered recipe generation.
  5. Before investing significant time and resources, conduct a pilot test with a small group of target users. This will give you invaluable feedback on your app's accuracy, usability, and overall value proposition. Based on user feedback from similar products, accuracy in calorie estimations, especially for home-cooked meals and dishes with mixed ingredients, is crucial. Prioritize this during your pilot test.
  6. Create content to grow awareness for the benefits of your product. Calorie tracking apps often face an uphill battle to gain users. Content can show how your app improves your customers' lives. Make sure to include real-world examples that your users can relate to. Ensure you tailor the content for your specific customer segment.

Questions

  1. Given the existing solutions in the market, what is your plan to ensure the AI in your app provides calorie estimations with significantly higher accuracy, especially for complex meals, compared to current offerings?
  2. Considering the low engagement observed in similar calorie tracking apps, how will you create a user experience that is not only accurate but also genuinely engaging and motivates users to consistently track their food intake over the long term?
  3. Many users are concerned about the sugar and oil content of their food. Given the user's feedback on similar products, what is your plan to provide users with a more comprehensive nutritional breakdown, including macronutrients, sugar, and oil content?

  • Confidence: High
    • Number of similar products: 24
  • Engagement: Low
    • Average number of comments: 1
  • Net use signal: 11.6%
    • Positive use signal: 15.3%
    • Negative use signal: 3.7%
  • Net buy signal: -1.8%
    • Positive buy signal: 0.0%
    • 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.

Similar products

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Users appreciate the app's features, particularly the instant food scanning and the engaging experience of calorie tracking with a virtual pet. One user reported weight loss using the app. A user inquired about the app's ability to scan home-cooked meals with mixed ingredients. There is also a request for more detailed calorie breakdowns, specifically including macronutrient information.

The primary criticism is that calorie breakdowns lack macronutrient information. Users expect a more detailed nutritional analysis beyond just calorie counts.

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📷 Track your calories ✅ Focus on simplicity: Upload photos of your meals and receive detailed calorie and nutrient information. ✅ Personalized tracking: Adjust your nutritional goals based on your individual profile.

Users praise the simplicity and ease of use of the Track-Calories.com tool, highlighting its accuracy. A suggestion was made to convert pricing to the user's location.

The primary criticism from the Product Hunt launch feedback is the need for improvement in pricing, specifically the conversion of prices to the user's location for better clarity and user experience.

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Track your calories easily by saying what you ate

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Hi guys,I created this app to solve one of my own problems: I found calorie tracking to be a real pain.Now, instead of scanning barcodes, scrolling through lists, or taking photos of food, I just speak into my phone (or computer), tell Calorio what I’ve eaten, and it handles the rest. It uses OpenAI’s APIs to process audio and map foods to calories.It’s free to use without an account, but if you want it to tally daily calories and remember everything you’ve eaten, you’ll need to sign up with your Google account and subscribe (you get 7 days for free).I would really appreciate any feedback; please let me know if you have any questions, issues or suggestions.Thank you! Paul.

Users find calorie tracking challenging and express a preference for text input due to privacy and accuracy concerns. There's specific feedback on inaccurate calorie estimations, notably with chicken breast counts being off. While some users commend the app and appreciate the macros feature, there's a suggestion to look at MacroFactor for voice to text and AI enhancements.

Users criticized the product for privacy issues related to voice input, lack of text input options, unreliable nutritional labels, and inaccurate calorie estimations. The calorie estimation issue was highlighted multiple times, with suggestions for improvement through learning from previous logs. Additionally, users noted the absence of a macros tracking feature, pointing out that a similar feature is available in MacroFactor.

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