New ideaConsumer Savings
Delivery Price Duel (Brazil)
A free consumer tool that compares the final price of the same restaurant order across iFood, 99Food, Keeta and Rappi (coupons, fees, delivery time), fed by user-submitted carts and receipts rather than scraping.
Summary
BriefingHypothesis
Brazil's delivery war (iFood 55M registered users, Rappi 20M, plus 99Food and Keeta) makes final prices swing wildly: a UOL test found the same R$33.90 order cost R$38.88 on iFood and R$11.95 on 99Food, while a TechTudo test found R$15.98 vs R$17.16 and delivery in 54 vs 27 minutes.…
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I found restaurant-side fee calculators, but no consumer same-cart comparator for Brazil (one search; Hypothesis). India has many (QuickCompare 1M+ downloads) and a UAE user asked for one on Reddit (Oct 19). Revenue figures are hypotheses.
DescriptionSelf-reported
Product: a mobile-web page (and later a browser extension) where a user pastes or screenshots their cart from one app and gets 'what would this cost on the others' using crowd-sourced prices for the same restaurant/dish by CEP (postal code), with live coupon status for the day. Data comes from three safe sources: user-…
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submitted receipts/screenshots (OCR), a user-side browser extension that reads only the user's own cart, and public menu/fee data; no scraping behind login. SEO pages: 'iFood ou 99Food: qual e mais barato em <city/restaurant>'. Viral loop: shareable 'I saved R$X by switching' cards. Revenue hypotheses: self-serve sponsor slots for restaurants and delivery partners priced up with traffic, a R$9.90/month alerts tier, and affiliate-free by design to keep trust. Benchmarks that the category works: QuickCompare (India, 1M+ downloads, 100K MAU per its API site; Self-reported), CompareMyKart (Swiggy vs Zomato carts).
Target customer
Brazilian urban consumers who order delivery weekly and use 2+ apps
Pain
Prices, fees and coupons differ by app and change daily; comparing means opening several apps
Why this score
Why it scored lower: Data access/ToS and crowd cold start
MoatHypothesis
Crowd price data by CEP and restaurant. Each submitted cart improves coverage; SEO pages per restaurant/chain rank for 'qual app e mais barato'. Defensibility: data network effects plus local SEO; status: Hypothesis.
Demand signals
X posts (7 days)
n/a
Signal trend
n/a
Web demandVerifiedSelf-reported
UOL and TechTudo tests (Verified journalism) show large coupon-driven gaps; iFood 55M and Rappi 20M registered users (Self-reported); QuickCompare India 1M+ downloads (Self-reported); UAE Reddit ask (Oct 19).
Market and competitors
TAMHypothesis
R$ 60B (Hypothesis) — Brazil food-delivery GMV (order of magnitude). Method: Hypothesis; not a sourced report; replace with ABRASEL/iFood published figures in CD.
SAMHypothesis
R$ 1B (Hypothesis) — Savings value for frequent multi-app users. Method: ~5M multi-app users x ~R$200/year savings; hypothesis.
SOMHypothesis
R$ 300K ARR (Hypothesis) — 12-18 months. Method: ~300K monthly users x tiny sponsor/alert yield ≈ R$25K/month at best; low confidence.
Competitors
Restaurant-side fee calculators (Tamy, calculadoradelivery.com.br), coupon sites, India analogs (QuickCompare, CompareMyKart), journalists' manual tests
Business model
Monetization
Self-serve sponsor slots priced up with traffic; R$9.90/month alerts tier; no affiliate links
Revenue hypothesis (monthly)Hypothesis
Base ~$6K/month, conservative ~$1.5K/month at 12-18 months (Hypothesis)
Jobs to be done
When I'm about to order the same food that is on several apps at different prices, I want to see the real final price on each app with today's coupons, so I can order from the cheapest or fastest one without opening them all.
HXC
Camila · 27 · São Paulo: Orders delivery 3-4 times a week, has iFood, 99Food and Keeta installed and chases first-order coupons. Tired of opening three apps; would submit a cart screenshot if it told her the cheapest total in 10 seconds.
Build and go-to-market
Tech stack
Next.js on Vercel; OCR via a vision API; Supabase; Stripe; optional browser extension; GitHub cesarx2 (one repo)
GTM channels
SEO/GEO pages per restaurant/chain/city, share cards for WhatsApp/X, faceless community answers; no founder face, no paid ads
Risks
Main risk
Data access/ToS and crowd cold start
All risks
Value Risk: Attention — Press tests show gaps but final totals sometimes converge (R$45.72 vs R$46.20). | Usability Risk: Attention — Submitting a cart must take under 10 seconds. | Feasibility Risk: Risk — App ToS and anti-bot measures; crowd data cold start. | Business Viability Risk: Attention — Platforms may block or copy; low monetization per user.
Next validation step
Interview 30 heavy users; test a paste-your-cart prototype on 20 São Paulo restaurants
Sources
- tab.uol.com.br https://www.techtudo.com.br/comparativo/2026/06/ifood-ou-99-testei-taxas-tempo-de-entrega-e-precos-veja-o-melhor-edapps.ghtml https://quickcommerceapi.com/ https://clauday.com/article/9f618f17-9ef6-48de-a199-e258b5f15b9c https://tamy.ai/ferramentas/comparador-delivery
delivery, price-comparison, brazil, ifood, 99food, keeta, consumer-savings, seo, crowd-data, idea, new, below-bar
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Research, not investment advice. Numbers keep the label they were published with.