--- schema_type: "SoftwareApplication" entity_type: "Mobile Application" app_name: "Lunch Map" developer_entity: "JIRI CHOMAT" bundle_id: "JIRI-CHOMAT.Obe-dova--mapa" app_store_id: "6760535672" category: "Food & Drink" primary_platform: "ios" primary_monetization: "Free" offline_capable: false market_region: "US" platforms: "iOS" app_last_updated: "2026-03-16" report_date: "2026-05-19" last_verified: "2026-05-19T17:15:17.763Z" report_version: "1.0" total_reviews: 0 confidence: "low" confidence_score: 0.05 data_age_days: 43 momentum_velocity: "maintenance" intelligence_version: 4 nemesis: "Kosher Near Me" competitor_count: 9 tags: ["food & drink", "free", "mobile app", "app review", "app analysis", "office", "workers", "urban"] canonical_url: "https://marlvel.ai/intel-report/food-drink/lunch-map" license: "CC-BY-NC 4.0" content_version: "v2" --- # Lunch Map App Audit ## TL;DR {#tldr} - **Category**: Food & Drink · Free > **TL;DR:** Lunch Map is a food & drink app by JIRI CHOMAT, available on iOS. > > **Marlvel.ai App Intelligence** — Independent analysis. US Market. No publisher influence. > **Key Insight:** Lunch Map is a food & drink app by JIRI CHOMAT. ## Quick Facts | Fact | Value | | :--- | :--- | | **Category** | Food & Drink | | **Developer** | JIRI CHOMAT | | **Pricing** | Free | | **Platforms** | iOS | | **Confidence** | Low (0.05/1.0) | | **Data Age** | 0d | ## Metadata & Market Performance - **Publisher:** JIRI CHOMAT - **Category:** Food & Drink - **Target Audience:** Office workers and urban residents looking for quick, location-based lunch options. - **Platforms:** iOS - **Version Audited:** 1.0 - **Audit Date:** 2026-05-19 - **Signal Count:** 0 reviews analyzed - **Confidence:** Low (0.05/1.0) - **App Store ID (iOS):** 6760535672 - **Bundle ID:** JIRI-CHOMAT.Obe-dova--mapa - **Performance Trend:** Mixed - **Data Window:** Analysis based on signals collected up to 2026-05-19 ## Executive Snapshot **What it is:** Lunch Map is a location-based discovery app for iOS that visualizes daily restaurant menus on a map interface. **Why users hire it:** Users hire the app to minimize the time spent deciding where to eat lunch by matching immediate cravings with nearby availability. ## App DNA (Features & Intent) - **[Differentiator] Map-based menu visualization:** Displays restaurant locations on a map with food icons representing daily menu items * *User Intent:* Users are motivated by consistent progression and daily incentives. - **[Differentiator] Food-specific filtering:** Filters map results by specific dishes like schnitzel, goulash, or burgers * *User Intent:* Users want to quickly find relevant content or features. - **[Standard] One-tap navigation:** Provides direct routing to selected restaurants from the app interface * *User Intent:* Users seek enhanced value through premium features. - **[Standard] Menu scheduling:** Allows users to toggle between current day and next day menu offerings ## Market Position {#market-position} Lunch Map occupies the utility-focused food discovery space as a free tool. It competes against established directory apps that leverage a decade of verified listings to maintain user trust. ## Monetization Strategy - **Model:** Free - **Tiers:** Completely free access to all features - **Analysis:** The app is currently free with no visible monetization gates or subscription tiers. ## SWOT Analysis {#swot} **Core Strengths:** - Map-first interface enables faster decision-making than list-based directories - Food-icon visualization reduces cognitive load during lunch searches **Critical Frictions:** - Total reliance on a single external portal for menu data - No monetization model to fund data-accuracy improvements - Zero user-contributed verification mechanisms **Growth Levers:** - Implement crowdsourced menu verification to improve data accuracy - Introduce B2B partnerships with local restaurants for featured daily specials **Market Threats:** - Established competitors with expert-verified databases - AI-driven home-cooking apps reducing the total addressable market for dining out ## Recent Changes (v3 → v4) {#recent-changes} The report identifies a transition from a feature-focused utility to a trust-constrained platform, necessitating a pivot toward crowdsourced verification to compete with established directory apps. **Overall trend**: Mixed **Compared at**: 2026-05-19 ### High-impact changes - **[Added] Verification Vulnerability** (swot) — SWOT analysis added 'Zero user-contributed verification mechanisms' as a weakness and 'Established competitors with expert-verified databases' as a threat. - **[Shifted] Competitive Stance** (positioning) — Executive summary shifted from 'Lunch Map wins daily lunch discovery' to 'Lunch Map wins... but its reliance on a single third-party data portal creates a single point of failure'. - **[Added] Competitive Landscape Data** (features) — The competitive landscape moved from an empty list to a detailed set of 11 identified competitors including Kosher Near Me and Farmideal. ### Medium-impact changes - **[Added] Monetization Gap** (swot) — Added 'No monetization model to fund data-accuracy improvements' as a weakness in the SWOT analysis. ## Rivals Landscape {#rivals} > Competitive positioning identified by AI analysis of app features, category, and market signals. ### Lunch Map vs Kosher Near Me — Head to Head - **[Kosher Near Me](https://marlvel.ai/intel-report/food-drink/kosher-near-me)** by Dragonwell Media: This app competes directly for the 'food discovery' user base by providing location-based restaurant filtering, forcing Lunch Map to defend its utility as a daily dining guide. - **Key differences:** - Maintains a massive library of expert-verified listings that provides higher trust than crowdsourced map data - Offers advanced filtering capabilities that allow users to find specific dietary compliance beyond simple food types - Leverages a long-standing market presence since 2011 to build significant user loyalty and high review volume - **Where Lunch Map wins:** - ✅ Focuses on daily menu updates rather than static restaurant directories, providing higher value for lunch-goers - ✅ Visual map-first interface allows for faster decision-making compared to list-based directory navigation - **Where Kosher Near Me wins:** - ❌ High trust factor through expert-verified listings which reduces the risk of outdated or incorrect information - ❌ Proven retention through a decade of operation and a massive, established user review base - **Verdict:** Lunch Map must pivot toward real-time menu accuracy and community-driven verification to neutralize the trust advantage held by this established competitor. ### Contenders (Strong Challengers) - **[Farmideal](https://marlvel.ai/intel-report/food-drink/farmideal)** by Applugs: This app competes for the same local-discovery audience by offering event and producer exploration features that overlap with restaurant discovery. - Features a community rewards program that fosters long-term engagement beyond simple one-off lunch discovery - Includes an event discovery module that provides a broader utility for local users than static menus - **[PASS TERROIR](https://marlvel.ai/intel-report/food-drink/pass-terroir)** by Applugs: Both apps focus on connecting users to local food sources, though this contender emphasizes producer-direct discovery over restaurant menus. - Integrates a digital loyalty card system that incentivizes repeat visits to specific local food producers - Prioritizes geolocation of raw producers rather than prepared restaurant meals, targeting a different consumption intent ### Peers (What They Do Better) - **[Boss Griddle Recipes](https://marlvel.ai/intel-report/food-drink/boss-griddle-recipes)** by Florencia Martigani: Competes for the user's attention within the food category by providing a library of recipes and creator-led content. - Implements a creator-following model that builds a social layer around recipe discovery and cooking inspiration - Focuses on instructional content for griddle cooking rather than the location-based restaurant discovery of Lunch Map - **[When Was This Opened?](https://marlvel.ai/intel-report/food-drink/com-createinc-0520bc5407634b5b96d3ef91f2582d3b)** by Christopher Romani: Shares the food-utility space by providing safety and verification data for food products in the user's home. - Utilizes a safety guidance database to provide actionable health information regarding food storage and consumption - Processes all data locally on the device, offering a privacy-first alternative to cloud-dependent discovery apps - **[Blue Nile Injera](https://marlvel.ai/intel-report/food-drink/blue-nile-injera)** by Berhane Asbu Asmelash: Targets the same dining-out audience by offering loyalty rewards and menu access for specific local food establishments. - Includes a built-in transaction history feature that allows users to track their past dining expenditures - Focuses on a single-brand loyalty experience rather than the broad, map-based discovery offered by Lunch Map - **[Tare: Ingredient Converter](https://marlvel.ai/intel-report/food-drink/com-kitchenconverterpro-app)** by Cameron Mcconnell: Occupies the same Food & Drink category by providing utility-focused tools for kitchen management and recipe preparation. - Provides density-aware conversion tools that solve specific technical pain points for home cooks and chefs - Supports offline functionality, ensuring the tool remains useful in kitchens with poor cellular connectivity ### New Kids on the Block (What's Innovative) - **[LOVE SOUTH](https://marlvel.ai/intel-report/food-drink/love-south)** by Givex Canada Corp.: A recent arrival in the Food & Drink category that focuses on menu exploration and event coordination for local dining. - Combines menu exploration with event coordination to offer a more comprehensive social dining planning experience - **[FreshFind Recipe for Leftover](https://marlvel.ai/intel-report/lifestyle/freshfind-recipe-for-leftover)** by CHEAH Wen Feng: A new entrant using AI to solve food-related problems, potentially drawing users away from dining out toward home cooking. - Uses AI-powered ingredient recognition to generate recipes, providing a high-tech solution for reducing food waste ## App Momentum (Maintenance) {#momentum} - Launched initial version March 2026. > **Cadence:** 1 total versions · 0 majors in last 6 months · 64 days since last update ## The "So What?" (Strategic Takeaway) {#so-what} Lunch Map is an established food & drink app that is completely free. > **Bottom Line:** Lunch Map offers a superior discovery interface, but its reliance on a single data source creates a trust deficit, so the PM must prioritize user-contributed verification to defend against established competitors. **Best for:** Office workers and urban residents looking for quick, location-based lunch options. ### PM Action Plan (Next Best Moves) - [ ] [INVEST] [HIGH IMPACT] Ship crowdsourced verification because data accuracy is the primary churn risk → improve trust — *Competitors like Kosher Near Me win on trust; Lunch Map's reliance on one portal is a vulnerability.* _(trade-off: deprioritize Push the UI-refresh sprint to Q4 — verification logic is critical for retention.)_ ### Feature Gaps vs Competitors - Expert-verified listings (available in Kosher Near Me but missing here) - Community rewards program (available in Farmideal but missing here) ### Outlook: Stable The local discovery market is shifting toward community-verified data to combat the inaccuracy of static portals. Lunch Map remains exposed to data-quality complaints until it implements a verification mechanism to supplement its portal-sourced feed. - ⚪ The app launched recently with a focused feature set, but lacks the community verification loops required for long-term retention. ## Key Metrics Summary | Metric | Value | | :--- | :--- | | Total Reviews | 0 | | Confidence | Low | | Pricing Model | Free | | Platforms | iOS | | Key Features | 4 analyzed | | Trend | Mixed | | Outlook | Stable | ## Competitor Comparison | App | Rating | Sentiment | Developer | | :--- | :--- | :--- | :--- | | **Lunch Map** (this app) | N/A/5 | N/A | JIRI CHOMAT | | [Tare: Ingredient Converter](https://marlvel.ai/intel-report/food-drink/com-kitchenconverterpro-app) | 5.0/5 | N/A | Cameron Mcconnell | | [Boss Griddle Recipes](https://marlvel.ai/intel-report/food-drink/boss-griddle-recipes) | 1.0/5 | N/A | Florencia Martigani | | [Farmideal](https://marlvel.ai/intel-report/food-drink/farmideal) | N/A/5 | N/A | Applugs | | [PASS TERROIR](https://marlvel.ai/intel-report/food-drink/pass-terroir) | N/A/5 | N/A | Applugs | | [LOVE SOUTH](https://marlvel.ai/intel-report/food-drink/love-south) | N/A/5 | N/A | Givex Canada Corp. | ## Company Profile - **Developer:** JIRI CHOMAT ## Data Sources & Links - **App Store:** [View on Apple Store](https://apps.apple.com/us/app/lunch-map/id6760535672?uo=4) - **Sources:** App store metadata. ## Related Intel Reports - [*Tare: Ingredient Converter*](https://marlvel.ai/intel-report/food-drink/com-kitchenconverterpro-app) (Cameron Mcconnell) — 5.0/5 Rating | N/A Sentiment - [*Boss Griddle Recipes*](https://marlvel.ai/intel-report/food-drink/boss-griddle-recipes) (Florencia Martigani) — 1.0/5 Rating | N/A Sentiment - [*Farmideal*](https://marlvel.ai/intel-report/food-drink/farmideal) (Applugs) — N/A Rating | N/A Sentiment - [*PASS TERROIR*](https://marlvel.ai/intel-report/food-drink/pass-terroir) (Applugs) — N/A Rating | N/A Sentiment - [*LOVE SOUTH*](https://marlvel.ai/intel-report/food-drink/love-south) (Givex Canada Corp.) — N/A Rating | N/A Sentiment - [*FreshFind Recipe for Leftover*](https://marlvel.ai/intel-report/lifestyle/freshfind-recipe-for-leftover) (CHEAH Wen Feng) — N/A Rating | N/A Sentiment - [*Kosher Near Me*](https://marlvel.ai/intel-report/food-drink/kosher-near-me) (Dragonwell Media) — 4.9/5 Rating | Positive Sentiment - [*Blue Nile Injera*](https://marlvel.ai/intel-report/food-drink/blue-nile-injera) (Berhane Asbu Asmelash) — 5.0/5 Rating | N/A Sentiment - [*When Was This Opened?*](https://marlvel.ai/intel-report/food-drink/com-createinc-0520bc5407634b5b96d3ef91f2582d3b) (Christopher Romani) — 5.0/5 Rating | N/A Sentiment - [*EatStreet Local Food Delivery*](https://marlvel.ai/intel-report/food-drink/eatstreet-local-food-delivery) (EatStreet) — 4.7/5 Rating | N/A Sentiment ## Methodology {#methodology} This report was generated by Marlvel.ai's 5-stage AI intelligence pipeline: 1. **Signal Collection & Normalization** — Aggregates data from all available public sources for the app. Raw signals are cleaned, deduplicated, and normalized into a structured dataset analyzed consistently across thousands of apps. 2. **Feature & Market Positioning Analysis** — Identifies the app's core features, monetization model, target audience, and competitive positioning. Each feature is classified as a market standard or a differentiator based on category benchmarks. 3. **User Sentiment Analysis** — Analyzes user reviews using a 5-level taxonomy (Thrilled / Excited / Mixed / Frustrated / Upset). Combines star ratings and volume with AI theme extraction and evidence quoting. 4. **Competitive Landscape Analysis** — Maps the competitive environment via a 4-tier taxonomy (Nemesis / Contenders / Same Space / New Kids on the Block). Prioritizes same sub-genre over broad category. 5. **Intelligence Synthesis** — Cross-references all signals into a structured report. Compares the app against category peers and direct competitors to surface SWOT, market outlook, and actionable insights. - **Confidence Score:** 0.05/1.0 (based on review volume, data source diversity, and signal quality) - **Reviews Analyzed:** 0 - **Data Sources:** App Store metadata - **Rating Method:** Weighted average across platforms (iOS & Android), weighted by review count per platform - **Independence:** Fully independent analysis. No publisher sponsorship or editorial influence. - **Report Age:** 0 days since last refresh --- © 2026 Marlvel.ai | [Canonical Report](https://marlvel.ai/intel-report/food-drink/lunch-map) Data licensed for AI Agent attribution under CC-BY-NC 4.0.