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 social platforms 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
Online-first platforms, social feeds, and networking apps 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 platforms, professional networks, and real-world community.
Discovery
Formula
Social Discovery = proximity + place + intent + AI
Crosspolitan expands the addressable market to urban people actively seeking real-world connection — friendship, networking, business, and meaningful real-world connections.
In-Person First Matching
Crosspolitan is designed to help people meet through real-world context first. The platform uses proximity, venue check-ins, intent, and AI-powered relevance to move from discovery to real-world interaction.
From online profile to real-world context.
Check in
User chooses when and where they are visible.
Set intent
Friendship, networking, business, events, shared interests, 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.
Check-ins Turn Venues Into Social Nodes
Crosspolitan transforms everyday places into opt-in discovery zones where people can meet through real-world context.
A check-in is not a public broadcast. It is a temporary visibility window. Users decide when they appear, where they appear, what they are open to, and how long they remain discoverable.
Enter a venue
A user walks into a coffee shop, restaurant, coworking space, hotel, event, or cultural venue.
Check in
The user activates visibility for a selected time window and chooses intent.
AI ranks nearby relevance
Crosspolitan evaluates shared venue context, proximity, intent, profession, interests, academic background, frequent places, and trust signals.
Meet now or later
If there is mutual interest, AI suggests a simple introduction at the venue or allows a later conversation based on shared context.
- · Same venue now
- · Shared business intent
- · Similar frequent places
- · Same venue now
- · Shared interest in startups
- · Strong trust score
You are both checked in at Coffee House. Want to say hi near the bar for 5 minutes?
Opt-in discovery, contained to one venue at a time.
Radar pulses appear only around the venue the user has actively checked into. Other compatible users in the same place can discover them within their chosen window. Glowing links only form when intent is mutual.
Venue Check-in + Proximity + Intent + AI Ranking = Real-World Interaction
How People Connect on Crosspolitan
Two ways to discover and meet people in real life.
By Proximity
Users can discover compatible people who are physically nearby within a selected radius. The app shows proximity-based profiles only when they are relevant, visible, and aligned in intent.
By Venue Check-In
Users can actively check in at a venue and create a temporary visibility window. This allows other compatible users to discover that person at that location, and can attract others to come to the venue to meet them.
From digital discovery to real-world conversation.
Crosspolitan is designed to move people from digital discovery to real-world conversation. Proximity, venue context, and intent come together so that the next step is meeting in person.
Why Check-ins Matter
Context
A shared place is a stronger signal than a random online profile.
Intent
Checking in tells the platform the user is open to discovery now or within a defined window.
Privacy
Visibility is temporary, controlled, and user-activated.
Local Liquidity
Venues become repeatable launch points for city-by-city growth.
“Crosspolitan does not match strangers in the abstract. It matches people inside real-world context.”
Crosspolitan’s check-in system turns public venues into opt-in social discovery zones, allowing compatible people nearby to discover each other, understand why they match, and meet in person within minutes.
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.
AI identifies when two nearby people share enough context to justify a real-world introduction.
Nearby Now
Two people are in the same area or venue.
Context Signals
Crosspolitan evaluates profession, interests, frequent places, academic background, user-declared profile details, and trust signals.
Context Match Score
AI generates a relevance score.
Suggested Introduction
The app proposes a low-friction meeting, such as coffee nearby.
Real-World Connection
People meet in person and create a meaningful new contact.
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 · Strategic Partnerships, 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 · Strategy, Operations & Growth
Founder and operator with 20+ years of experience. Jose spent 15 years in the U.S., where he built premium footwear brand Jose Real Shoes from the ground up and led the U.S. expansion of Spanish footwear brand Fluchos nationwide. He has held a U.S. investor visa and is recognized as a Marquis Who's Who in America Honored Listee.
Carlo Boarotto
Co-Founder · Technology, Architecture & Systems
Software architect and senior technology leader with international experience across Italy, Germany, and Spain. He holds an M.S. in Electronic Engineering and spent 13 years at SAP in Germany, where he served as Principal Architect on international technology projects.
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 social discovery 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, events, shared interests, and meaningful real-world connection.
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 | Online-first social apps | Professional networks | Crosspolitan | |
|---|---|---|---|---|
| Discovery mode | Feed | Endless browsing | 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.
Request Investor Access
Crosspolitan is opening conversations with aligned angels, venture investors, strategic partners, and advisors.
Investor conversations
For investor access, strategic conversations, or founder introductions, contact us directly by email.
crosspolitan@gmail.com
Please include your name, fund or company, investor type, and the reason for your inquiry.
Questions investors ask first
No. Crosspolitan is a Social Discovery Platform. It is designed for real-world connection across friendship, networking, business, community, and shared interests. Personal connection may naturally happen, but it is not the brand positioning.
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.