Mobile App Developer Needed to Build V1 No-Code MVP for REAISM

REAISM V1 — No-Code Mobile App MVP Developer I’m looking for an experienced no-code/low-code mobile app developer to build the V1 MVP of REAISM, a platform combining challenges, advanced matching, AI-assisted/reverse search, social media, real estate, entertainment/voice opportunities, and products/services discovery. This is not the full future vision of REAISM. I want a focused, functional V1 that we can launch, put in users’ hands, collect feedback from, and improve. REAISM V1 — Expanded Product & Feature Scope REAISM V1 is a no-code/low-code mobile-first MVP built around five connected products: RASmatch RASearch RASchallenge Ask Buzz/M-Guide RASplatform The V1 experience should be designed around one major priority: Challenges first. Social media second. Social features should primarily help users discover challenges, participate, collaborate, share progress, meet relevant people, and continue deeper into the REAISM ecosystem. ⸻ 1. RASmatch — Advanced Matching Purpose RASmatch is the intelligent matching layer that operates across RESolution, AIR, and SMU. Instead of users manually searching through hundreds of profiles, properties, services, opportunities, communities, or challenges, RASmatch should analyze relevant user information and rank the options that appear to be the best fit. The goal is to answer: “What is most relevant to this person right now?” RASmatch should eventually become one of the shared intelligence systems underneath REAISM. V1 User Profile Data Users should be able to provide structured information during onboarding and later edit it through their profile. Potential data includes: Location Search radius Interests Goals Skills Occupation Desired occupation Industry Experience Education Personality/preferences Communication preferences Languages Availability Budget Price range Property requirements Services needed Challenge interests Creator interests Entertainment interests Tags Categories User-selected priorities Saved items Likes/dislikes Previous activity The V1 does not have to collect every field immediately. The architecture should allow new matching attributes to be added later. Matching Logic The V1 should use a rules-based and weighted scoring system rather than requiring an expensive proprietary machine-learning system. For example: A user might be matched with another person based on: Location Shared interests Compatible goals Relevant skills Availability Experience Personality/preferences A property could instead prioritize: Location Price Square footage Property type Parking Amenities Distance User requirements Each matching category should therefore be able to use different weights. Match Score Where practical, recommendations should include a simple match indicator. Example: 93% Match Why it matches: Within preferred location Meets budget Matches 5 selected interests Meets experience requirements Available during preferred schedule Users should ideally understand why they received a recommendation rather than receiving a mysterious AI-generated percentage. Must-Haves vs Preferences RASmatch should distinguish between requirements and preferences. Example: Must have Under $2,200/month Allows dogs Two bedrooms Preferred Gym nearby Covered parking Less than 20-minute commute A property violating a must-have should normally be excluded. A property missing a preference could still appear with a lower score. RESolution Matching RASmatch can match users with: Residential properties Commercial properties Buyers Sellers Tenants Landlords Roommates Agents Investors Vendors Contractors Real-estate professionals Example: A user looking for a martial-arts studio could be matched with spaces based on square footage, ceiling height, monthly cost, distance, parking, use type, and related requirements. AIR Matching AIR can match: Singers Rappers Musicians Producers Voice actors Speakers Teachers Studios Venues Retreats Private getaways Creative collaborators Performance opportunities Example: A singer could be matched with a producer based on genre, budget, location, experience, goals, style, and availability. SMU Matching SMU can match users with: People Businesses Creators Services Products Communities Challenges Collaborators The purpose is to make social discovery more intentional than simply suggesting popular accounts. User Feedback Users should be able to: Like a match Save it Hide it Reject it Contact/message where applicable Change matching preferences * Request new recommendations These interactions should be stored so future versions can become more personalized. V1 Success Goal The V1 does not need to prove that REAISM has invented a new AI algorithm. It needs to prove: REAISM can use profile data, filters, tags, p