The AI-Powered Real-World Social Layer
Crosspolitan turns shared places into trusted, in-person connections through proximity, check-ins, intent, and AI-powered contextual matching.
Most apps match profiles. Crosspolitan matches situations. When two compatible people are nearby, the platform explains why they should meet and suggests a real-world introduction.
Privacy-first · In-person-first · AI-powered · City-by-city rollout
People are surrounded. Still disconnected.
Urban anonymity
Cities create density, but not connection. People cross paths daily without a safe reason to start a conversation.
App fatigue
Dating apps, social feeds, and networking platforms optimize screen time instead of real-world outcomes.
Lost social context
Remote work, streaming, and online-first behavior have reduced the spontaneous interactions that used to build relationships.
Crosspolitan defines Social Discovery
A new category between social, dating, and networking. Crosspolitan expands the addressable market beyond dating by serving people who want to meet for friendship, networking, business, or dating when there is real-world context.
Discovery
Formula
Social Discovery = proximity + place + intent + AI
Crosspolitan expands the addressable market to urban people actively seeking real-world connection, whether the outcome is friendship, business, networking, or dating.
From online profile to real-world context.
Check in
User chooses when and where they are visible.
Set intent
Friendship, networking, business, dating, or open to context.
Discover nearby
Short-distance radar shows relevant people nearby or checked in at the same venue.
Mutual signal
Users express interest with a lightweight opt-in action.
Meet in person
The product is designed to create real-world interactions, not endless scrolling.
Monthly Active Users with at least one real-world interaction.
AI matches based on what matters.
Crosspolitan uses proprietary real-world signals to turn proximity into relevant, safe, in-person meetings.
Most apps match profiles. Crosspolitan matches situations. Our AI analyzes proximity, shared places, intent, interests, background, and trust signals to identify when two people nearby should actually meet.
Check-in
User chooses where they are visible, how long, and what they are open to.
Signal Layer
AI reads proximity, venue context, interests, background, intent, and trust signals.
Compatibility Score
AI ranks nearby people using a transparent context match score.
- Same venue now
- Shared intent
- Trust score: high
Why This Match
Same venue, shared intent, overlapping background, strong trust score.
Instant Meeting Suggestion
AI proposes a simple in-person meeting with one-tap accept.
AI makes the right introductions. People make it real.
- Coffee House
- Coworking Space
- Rooftop Bar
- Same venue now
- Shared business intent
- Both frequent coworking spaces
- Strong professional overlap
- Safe visibility window
- Coffee House
- Coworking Space
- Art Gallery
From match to meet in minutes.
Presence becomes context, not raw location.
Crosspolitan reveals who is nearby, where they are, and whether the moment is right to meet. Visibility is opt-in, time-boxed, and user-controlled at every step.
The AI compatibility engine
Six signal groups feed one transparent context match score.
Profession
Similar industries, roles, goals, founder/operator overlap.
Interests
Shared passions, hobbies, and lifestyle preferences.
Academic Background
Education, alumni networks, schools, knowledge domain.
Places Both Frequent
Cafes, gyms, galleries, coworking spaces, hotels, events.
Personal Details
Language, lifestyle, values, city status, profile details.
Behavioral & Trust
Verification, response quality, visit history, account behavior.
“Crosspolitan’s moat is proprietary real-world signals.”
The moat is not the AI model alone. It is the proprietary IRL signal graph Crosspolitan builds through check-ins, co-presence, venues, timing, intent, and post-meet feedback.
The AI matching example
Ana checks in
At Coffee House. Visible for 60 minutes. Intent: Networking and Friendship.
David arrives
Same venue. Intent: Business and Networking. AI detects real-time proximity and context overlap.
AI ranks the match
Score 95%. Same place now, shared professional intent, overlapping interests, strong trust score.
AI suggests the meet
“You are both at Coffee House now. Want to say hi for 5 minutes near the bar?”
AI makes the right introduction. People make it real.
Why AI makes Crosspolitan defensible
Discovery Intelligence
AI ranks the most relevant nearby people using place, intent, proximity, timing, interests, professional and academic background, and trust signals.
Activation Intelligence
AI converts a match into a real-world meeting by suggesting the right opener, place, time, and next step.
Trust Intelligence
AI detects risk signals, spam, scams, harassment, fake behavior, and low-quality accounts before they damage the network.
From MVP to Proprietary IRL Signal Graph
Crosspolitan scales city by city, using AI to convert proximity, check-ins, and intent into real-world meetings.
Ramp-Up
Objective. Turn the concept into a build-ready company.
- Final MVP scope
- UX flows
- Product architecture
- AI matching logic
- Privacy framework
- Investor website
- Legal setup
- Fundraising materials
Product is no longer just an idea.
MVP Build
Objective. Ship the first working product.
- User profiles
- Intent settings
- Visibility window
- Short-distance radar
- Venue check-ins
- AI match ranking v1
- Basic safety scoring
- Analytics dashboard
Working demo investors can touch.
Controlled Beta
Objective. Validate that people move from match to real-world meeting.
- Private user cohort
- First venue network
- Real-time matching
- AI meeting suggestions
- Safety reporting
- User feedback loop
- Check-in analytics
Evidence that Crosspolitan can create real-world meetings.
First City Launch
Objective. Create local liquidity in one city.
- Launch campaigns
- Local ambassadors
- Venue activations
- Referral loops
- Premium plan test
- AI concierge test
- Community events
- Retention dashboard
First repeatable city playbook.
Multi-City Expansion
Objective. Replicate the model across additional city clusters.
- Additional city launches
- Venue intelligence dashboard
- Event curation engine
- Paid conversion optimization
- Local partner playbook
- AI personalization improvements
City-by-city scalability.
International Scale
Objective. Build a proprietary IRL signal graph across major cosmopolitan hubs.
- Regional expansion
- Professional networking features
- Venue partner monetization
- Advanced AI matching
- Trust and safety automation
- Premium membership growth
- Data-driven city launch engine
Defensible network effects from proprietary real-world signals.
Milestone Gates Investors Can Underwrite
Each gate is a binary check. Crosspolitan progresses only when the previous gate is proven, not assumed.
Product Gate
Users understand the product in under 30 seconds.
AI Gate
AI improves match relevance versus basic filters.
Meeting Gate
Users move from match to real-world meeting.
Safety Gate
Report rate, bad actor rate, and abuse cases stay controlled.
Retention Gate
Users return because the app creates real-world opportunities.
Monetization Gate
Paid features increase value without damaging trust.
Expansion Gate
The first city playbook can be repeated in another city.
Multiple revenue streams. One behavior engine.
The model is designed to combine consumer subscription economics with local marketplace monetization.
Why now
- Online-first connection is broken.
- Urban loneliness and app fatigue are structural problems.
- Location and intent can create stronger real-world matching.
- The next social platform will not be another feed. It will connect digital identity to physical presence.
Why Crosspolitan can win
- Clear category positioning: Social Discovery.
- In-person-first product architecture.
- Privacy and user control by design.
- City-by-city rollout creates focused liquidity before scale.
Founding Team
A cross-border founding team combining consumer brand building, international operations, intellectual property, and software architecture.
Airam Matheus
Co-Founder · Strategy, Communication, Legal & IP
International entrepreneur and intellectual property lawyer with experience across the United States and Europe. She brings strategic communication, legal, IP, and institutional experience, including work with international law firms and the European Union Intellectual Property Office.
Jose Real
Co-Founder · Brand, Growth & International Expansion
Founder and operator with 20+ years of international experience across the U.S. and Europe. After nearly 15 years in the U.S., he founded Jose Real Shoes and built premium footwear distribution across 200+ retail locations, while also leading U.S. expansion for Spanish footwear brand Fluchos.
Carlo Boarotto
Co-Founder · Technology, Architecture & Systems
Software architect and senior technology leader with experience in Italy, Germany, and Spain. He holds an M.S. in Electronic Engineering and has worked with major organizations including the European Southern Observatory, automotive companies, an EU agency, and SAP, where he served as Principal Architect.
Crosspolitan is being built by founders who understand brand, law, systems, international markets, and technology execution. The founding team combines the skills required to build trust, launch city by city, and turn a social concept into a scalable platform.
A massive behavior shift is already happening.
Social media user identities worldwide
Projected online dating application market by 2030
LinkedIn FY2025 revenue
The opportunity is not to build another vertical app. The opportunity is to own the real-world interaction layer that connects digital intent with physical presence.
Sources: DataReportal, Grand View Research, Microsoft FY2025 Annual Report.
Built around real-world liquidity.
Short-Distance Radar
Users discover people nearby within a selected radius.
Venue Check-ins
Restaurants, cafes, coworkings, events, gyms, hotels, galleries, and nightlife venues become social nodes.
Purpose-Based Profiles
Users can signal whether they are open to friendship, networking, business, or dating.
Contextual Matching
Shared places and similar urban behavior create stronger matching context.
Privacy Controls
Users decide when they are visible, where they are visible, and when they disappear.
Safety and Moderation
Verification, reporting, blocking, visibility limits, and abuse controls.
Less feed. More intent.
| Traditional social media | Dating apps | Professional networks | Crosspolitan | |
|---|---|---|---|---|
| Discovery mode | Feed | Swipe | Search | Proximity & check-ins |
| Primary behavior | Consume content | Match online | Message cold | Meet through shared context |
| User context | Follower graph | Profile claims | Job identity | Place, proximity & intent |
| Business model | Attention | Scarcity | Recruiting | Real-world social utility |
Calm. Controlled. Transparent.
User-Controlled Visibility
Users decide when they appear, where they appear, and for how long.
Temporary Location Context
Raw location is not the product. Shared context and short visibility windows are.
Transparent Matching
Users see why a match is suggested.
Safety by Design
Trust scoring, reporting, blocking, and behavior anomaly detection protect the network.
Crosspolitan is building the intelligence layer for real-world social discovery. Every check-in, shared place, mutual signal, and post-meet feedback loop makes the network smarter, safer, and harder to copy.
Visibility is controlled. Context is temporary.
User-controlled visibility
Users choose when they appear and can pause visibility instantly.
Place-based context
The product uses shared locations and check-ins, not endless background exposure.
Data minimization
Only essential data is used for the experience.
Safety layer
Reporting, blocking, verification, and moderation are part of the core architecture.
Privacy is not a compliance checkbox. It is a product moat.
A new traffic layer for venues.
Crosspolitan can turn cafes, coworkings, hotels, restaurants, galleries, events, and premium local spaces into connection hubs.
Venue discovery
Users have a reason to visit places where relevant people are present.
Check-in activation
Venues can host social discovery moments, member events, and themed meetups.
Measurable traffic
Aggregated, privacy-safe analytics can help venues understand engagement and activation.
City-by-city liquidity, not global noise.
- 1Phase 1
MVP build
Product prototype, onboarding, radar, check-ins, privacy controls, analytics.
- 2Phase 2
Seed community
Urban professionals, expats, founders, creators, freelancers, students, coworking members, and socially active locals.
- 3Phase 3
Venue network
Cafes, coworkings, hotels, restaurants, galleries, events, and local premium venues.
- 4Phase 4
First City Launch
Influencers, IRL activations, referral loops, launch events, venue partnerships.
- 5Phase 5
Repeatable city playbook
Expand city-by-city after proving density, retention, and real-world interaction rate.
An investor dashboard built around real-world outcomes
Monthly Active Users with at least one real-world interaction.
- Monthly active users with at least one real-world interaction
- Meet rate per active user per week
- Time to first meeting
- Check-ins per active user
- Match acceptance rate
- D7 retention
- D30 retention
- Paid conversion
- CAC per signup
- Revenue per venue partner
- Report rate
- Bad actor rate
Model scenario based on internal assumptions. Actual performance will depend on product validation, user adoption, paid conversion, CAC, retention, and market rollout execution.
Crosspolitan does not scale by chasing global noise. It scales by proving local liquidity, then repeating the city playbook. Every check-in, shared place, mutual signal, and post-meet feedback loop strengthens the proprietary IRL signal graph.
What the first capital unlocks
MVP release
Short-distance radar, check-ins, user profiles, mutual signals, safety controls.
First city beta
Controlled cohort, venue partners, activation calendar, local ambassadors.
KPI dashboard
Real-world interactions, check-ins, conversion, retention, safety reports, venue engagement.
Investor-ready scale plan
Repeatable launch playbook and roadmap for additional cities.
Currently raising to fund MVP and First City Launch. Exact terms available through investor access.
Invest in the real-world social layer.
Crosspolitan is preparing its MVP and first city launch. We are opening conversations with aligned investors, angels, and strategic partners.
For investor conversations: crosspolitan@gmail.com
Questions investors ask first
No. Crosspolitan is a Social Discovery Platform for friendship, networking, business, and dating only when real-world context exists.
The proprietary IRL signal graph. Shared venues, co-presence, timing, intent, trust signals, and feedback loops are difficult to copy.
Users control visibility, radius, intent, and time windows. Matching is based on temporary context and privacy-safe signals.
City by city. The goal is to prove local liquidity first, then repeat the launch playbook across cosmopolitan hubs.
MVP build, AI matching logic, safety layer, analytics, first city launch, venue activation, and investor-ready validation.