How MacroMate's 2,000+ Fast-Food Macros Are Verified
The 4 sourcing rules
Every entry in MacroMate's database passes four rules before it ships to users:
- Official source first. Every macro number is the restaurant's own published value — not an AI estimate, not a crowdsourced guess, not a scraped third-party calorie database.
- Order-level, not item-level. A "build" is one specific order (e.g. a bowl with named modifications), not a single menu item. Real users order builds, not items.
- Goal-classified. Every build carries a category tag: Cutting, Bulking, Maintenance, Keto, or Low Calorie. The classification follows protein-to-calorie ratio thresholds + practical fitness-tracker conventions.
- Re-verified on menu changes. When chains update their menus or nutrition pages, the affected builds are re-scored against the new official numbers.
See How MacroMate Gets Its Numbers for the full verification process.
What the data says, in aggregate
Across 2,000+ builds, five patterns emerge:
- The gap between best and worst macro order at the same chain averages 3.5x protein-to-calorie ratio.
- Removing bread, buns, or tortillas saves an average of 200–300 calories with near-zero protein loss.
- Sauces and sides account for more wasted calories than any other category — up to 750 cal/meal.
- Chicken-focused chains dominate cutting rankings; the protein-to-calorie ratio leaders cluster between 0.150 and 0.255.
- 50+ builds in our database exceed 700 calories with 50g+ protein — fast-food bulking is more viable than most coverage suggests.
The specific builds behind these aggregates — by chain, by goal, with exact ordering instructions — are in the app.
Categories tracked
Each build is tagged with exactly one of:
- Cutting — high protein-to-calorie ratio, designed for caloric deficit days.
- Bulking — high absolute protein with calories to support gain phases.
- Maintenance — balanced macro splits for sustainable everyday eating.
- Keto — protein + fat with under 10g net carbs per order.
- Low Calorie — anything that lands under 400 calories regardless of protein density.
What separates MacroMate's data from alternatives
- vs. MyFitnessPal: MFP relies on user-submitted entries. The same Chipotle bowl can vary by 800+ calories between entries because users guess at modifications. MacroMate builds are sourced from official numbers, then locked.
- vs. AI estimate apps (MenuFit, MacrosMap): Those use AI models to predict macros. Convenient at scale, but error compounds across a tracking week. MacroMate uses the source, not an estimate of the source.
- vs. nutrition databases (FatSecret, Eat This Much): Those track single items. MacroMate tracks the optimized goal-specific build with the modifier stack — the difference between "Chipotle chicken bowl" (one row) and "Chipotle Double Chicken Bowl with light rice, black beans, fajita veggies, fresh tomato salsa, extra lettuce" (a specific build users actually order for a specific goal).
Where the full hack inventory lives
The website's job is to explain the methodology and prove the data is real. The actual 2,000+ optimized builds — with their exact ordering instructions, by-goal categorization, modifier stacks, and macros — live in the iOS and Android app.
The app is free.
Download for iOS Download for Android
For press + research inquiries
If you're writing about fast-food nutrition trends, GLP-1 menu shifts, protein-per-dollar, or related topics, MacroMate is happy to provide aggregate findings, methodology details, and on-the-record commentary. Email willbrennanhart@gmail.com with the angle and we'll respond same-day with statistics you can cite.
Note: we don't share the raw per-build dataset publicly. The structured per-build data is the core of the app, which is what funds continued data verification.