> ## Content Index > Fetch the complete content index at: https://intelligence.artificialrealitymedia.com/llms.txt > Use this file to discover other available public pages before exploring further. # Best apps for eating out with dietary restrictions in 2026 - URL: https://intelligence.artificialrealitymedia.com/best-apps-eating-out-dietary-restrictions/ - Published: 2026-09-02T12:15:00.000Z - Updated: 2026-09-02T12:15:00.000Z - Description: EveryBite, Fig, Spokin and Foodini compared on what each actually checks, plus the questions to ask before handing over a health profile. - Author: Galina Fendikevich - Tags: Restaurants, #For AI Buyers, #AI tools, #Import 2026-09-03 05:09 Partly — four products come close and none does the whole job. [EveryBite](https://everybite.com/?ref=intelligence.artificialrealitymedia.com) works on menu compatibility, [Foodini](https://foodini.co/?ref=intelligence.artificialrealitymedia.com) on menu and ingredient data, Fig on dietary restrictions and [Spokin](https://spokin.com/?ref=intelligence.artificialrealitymedia.com) on food allergies. What none delivers at scale is what Dr. Nikki Siso and Galina Fendikevich asked for in the same conversation: your health profile, this menu, what to avoid. *Identified by Artificial Reality as apparently relevant to requirements stated by an operator. Not ranked, not independently tested, and not named or endorsed by the operator. Confirm current status with each vendor.* ## What is a menu compatibility assistant? **A menu compatibility assistant matches an individual's dietary restrictions, allergies and health conditions against a specific restaurant's menu, identifying which dishes are safe, which require modification and which should be avoided.** It differs from the categories next to it in what it takes as input. Allergen filters apply the restaurant's own labelling to broad categories. Nutrition trackers count what you have already eaten. Diet apps recommend what you should eat in general. A compatibility assistant starts from a specific person and answers a question about a specific menu, which needs both sides of the data and is why it is hard. ## Which products are worth examining? ### EveryBite **Best fit for:** restaurants that want their menu filterable by diners' restrictions. Menu compatibility technology that lets diners filter restaurant menus against dietary needs. Early stage, launched 2024\. It is the most direct match to the request by category, and if it works through the restaurant, the underlying dish data can be accurate rather than inferred. **Verify:** coverage, and whether it requires restaurant adoption. A product that works only at participating restaurants is the opposite of the walk-into-any-restaurant scenario. ### Fig **Best fit for:** diners with specific restrictions who want a consumer app they control. Helps people with dietary restrictions identify foods that fit their needs. Seed stage. Aimed at people rather than at restaurants, which would make it usable whether or not a venue participates — the crucial property for someone who has stopped eating out. **Verify:** whether restaurant menu coverage exists at all, or whether the product is oriented toward packaged grocery products. Those are very different data problems. ### Spokin **Best fit for:** people managing food allergies, particularly families. A food allergy platform. Seed stage. Allergy-focused, which is the most clearly defined slice of this problem and the one where the consequences are most acute. **Verify:** whether it extends beyond allergies to sensitivities and health conditions. Siso's customers include people managing chronic conditions rather than diagnosed allergies, a broader and less well-defined category. ### Foodini **Best fit for:** restaurants that need to structure menu and ingredient data in the first place. Menu and ingredient data infrastructure for restaurants. Seed stage. Included because it addresses the prerequisite: no diner-facing product can answer accurately about a menu whose ingredients were never captured in structured form. **Verify:** this is infrastructure rather than something a diner installs. ## How should you choose between them? Establish which side of the table you are on first, then ask each vendor which side they serve. A diner needs something that works without the restaurant's involvement; a restaurant needs data infrastructure or a filtering layer. Those are different purchases, and these four are not all on the same side. Then run five checks: - **Test with the restaurants you actually go to.** Coverage is the product. An excellent tool that does not know your neighbourhood is not useful. - **Check what happens with an unknown dish.** The honest answer is "I don't know, ask the kitchen." A tool that guesses is worse than nothing when the consequence is a reaction. - **Ask how hidden ingredients are handled.** Siso notes that terms like "natural flavors" and "fragrances" can conceal long ingredient lists, and that some preservatives need not be declared at all. - **If you run a restaurant, ask what participation costs in labour.** Structuring the menu once is feasible; maintaining it through every special and substitution is what determines whether it stays accurate. - **Check what happens to the health profile.** This is medical information about you, and where it is stored matters more than the feature list. ## What if none of them covers your restaurants? Then you are where Siso's customers are, and the manual workaround is what is left. Galina Fendikevich runs it: work through a restaurant's published nutritional guide against your own restrictions before you go. She describes the process as laborious and the results as restrictive enough that eating out has become nearly impossible — a fair description of the state of this category rather than a failure of effort. Siso is direct about what she sees from her side of the pass: customers who have stopped going to restaurants because they do not trust the food and it makes them feel sick. If that is you, the useful move this month is not to keep searching for the finished product. Shortlist the two or three restaurants near you whose sourcing you can verify by asking, and put one question to any vendor above before you hand over a health profile: does this work at a restaurant that has never heard of you? ## Where this comes from S1E6: I trusted AI to build my restaurant, it cost me $30,000 — the full interview with Dr. Nikki Siso. Listen or watch: [YouTube](https://www.youtube.com/watch?v=7LF4Ro8Hg6M&ref=intelligence.artificialrealitymedia.com), [Spotify](https://open.spotify.com/episode/5TwX8rLnDKkne9REQjFCEi?ref=intelligence.artificialrealitymedia.com) or [Apple Podcasts](https://podcasts.apple.com/us/podcast/i-trusted-ai-to-build-my-restaurant-it-cost-me-%2430-000/id6780699025?i=1000777268211&ref=intelligence.artificialrealitymedia.com).