a club-owned, ai-native ticketing platform

Your fans. Your data.
Your platform.

MioCa is a club-owned, data-sovereign ticketing & fan platform built for semi-pro and lower-tier soccer clubs. One shared codebase, an individualized instance per club — owned by the club, operated by us.

Club Owns the Data Predictable Pricing AI-Native Per-Club Instance EU Hosting
mioca@cloud — provisioning
# spin up a per-club instance
$ mioca init --club your-club-fc
 
region eu-central-1 (frankfurt)
data contract: club-owned
code license: club-owned
ai claude · openai · others
payments payone (sepa, dach)
 
$
// The one-line pitch

Built once. Individualized per club. Maintained by us. The all-in-one operating platform for clubs that incumbents structurally can't ship — ticketing today, CRM & marketing tomorrow.

Sebastian currently runs ticketing and lives the customer's pain daily. Adam (CTO) and Felix (CSO) round out a Germany-based founding team going full-time on close of funding.

~$20.6B
Global ticketing TAM (2024)
1
Shared codebase · individualized per club
100%
Club owns the data & code
2026/27
Launch season

Ticketing for smaller clubs isn't broken.
It's just not built for them.

Eventim, Ticketmaster, Reservix, ticket.io, vivenu, secutix — the incumbents run on per-ticket fees, templated UX, and platform-owned fan data. The clubs feel it every day. Fans rarely notice.

// 01
Clubs don't own their fan data.
It lives inside the platform vendor — not the club. When a club wants to run a campaign, the data is hostage to whoever sold them the ticketing.
// 02
Solutions are templated, not individual.
Smaller clubs can't afford a custom shop, so they're forced into one-size-fits-all setups that don't reflect the club's brand or workflows.
// 03
Per-ticket fees are a tax on success.
Costs scale with how many tickets you sell, not with what the club actually consumes from the platform. Successful seasons get punished.
// 04
Backends are clunky for the staff using them.
The people doing ticketing day-to-day — admin, finance, marketing — are stuck with generic tools designed for nobody in particular.
Why now. The AI cost curve makes it economical for a small founding team to ship individualized, per-club instances — work that used to require a 20-person engineering org. Incumbents are large and slow to retool for the long tail of small clubs. That gap is MioCa's window.

Ticketing today.
All-in-one club platform tomorrow.

A shared codebase, deployed as a dedicated cloud instance per club — individualized via configuration, not bespoke forks. Start with seat-map ticketing and payments; grow into the operating system for the club.

  • MVP
    Online shop with seat-map booking
    Per-club branding, individualized layouts, real seat-map UX — not a templated dropdown.
  • MVP
    Payments via Payone
    DACH-friendly fees, SEPA support, hosted checkout — MioCa stays out of PCI scope by design.
  • MVP
    Customer & fan database
    Owned by the club, contractually. Handed back on request or at contract end.
  • MVP
    Match-day access-control handover
    Clean transfer of sold tickets to the club's existing access-control hardware (Skidata, Axess, AVS, etc.).
  • Next
    CRM, newsletter & marketing modules
    Modular add-ons that turn the ticketing platform into the club's full fan-relationship stack.
  • Next
    Adjacent sports & geographies
    Basketball, handball, ice hockey. UK, rest of EU, US, UAE — same codebase, individualized per club.
Milestones
  • Today · 2026
    Prototype phase. First customer conversations underway.
  • Sept–Oct 2026
    MVP frozen · first club live · first revenue.
  • End of Year 1 (mid-2027)
    10 paying German clubs.
  • End of Year 2
    20–30 paying clubs · still Germany.
  • End of Year 3
    ~100 paying clubs · first non-German clubs onboarded.
  • Post-Year 3
    Adjacent sports: basketball, handball, ice hockey.

AI isn't a bolt-on.
It's how MioCa exists in the first place.

Two layers. Internally, AI lets a tiny founding team ship and operate individualized instances per club — work that used to need a 20-person engineering org. In the product, AI features ride a hybrid, model-agnostic architecture so we route the right model to the right job — Claude, OpenAI, and others — by cost, latency, and quality.

[AUTO-SEAT]
Auto-assigned tickets
Fans get smartly placed seats based on group size, history, and preferences — not a manual seat-picker grind on every match-day.
[PRE-CART]
Pre-filled carts
Fan history drives a pre-built cart at sale-open: usual seats, usual companions, usual extras. Click and confirm, instead of starting from zero.
[STRATEGY]
Per-club business strategy
AI-generated, per-club pricing & commercial recommendations at onboarding — the kind of analysis only top-flight clubs typically afford.
[OPS]
Individualization at scale
One shared codebase. Per-club configuration, branding, and workflows shipped without bespoke forks — that's the AI productivity dividend.
[VECTORS]
Embeddings next to data
MongoDB Atlas Vector Search keeps fan-profile, ticket-history, and content embeddings beside the operational data — no second DB to run.
[ROUTING]
Model-agnostic routing
Cheaper models for routine jobs, frontier models for high-value reasoning. No single-provider lock-in. Redundancy if one degrades.
// model layer Anthropic Claude OpenAI Gemini Mistral Open-source via Bedrock / Together

We sell to the club,
not the fan.

The buyer is admin, finance, marketing, and ticketing staff at semi-pro and lower-tier soccer clubs. The end user is the fan. The product has to work for both — but the contract is with the club.

// Launch leagues (Germany)
Tier 2
2. Bundesliga
Expansion target once early pilots prove the model.
Tier 3
3. Liga
Design partners. The first wedge.
Tier 4
Regionalliga
Every club a priority. Outreach intensifies post-prototype.
Tier 5
Oberliga
Affordable upgrade path from spreadsheets & bank transfer.
2026/27
Germany
Lower-tier launch market
Year 3+
UK · EU
First non-German clubs
Later
US · UAE
Same codebase. New leagues.
Post-Year 3
Adjacent sports
Basketball · handball · ice hockey

We move fast inside a window
incumbents can't turn into.

The thesis is simple: AI lowered the cost of individualized ticketing platforms; incumbents are large, profitable, and slow; the long tail of small clubs is structurally underserved. That gap closes when one player moves quickly enough to occupy it. We intend to be that player.

Dimension
Incumbents
MioCa
Data ownership
Platform owns fan data
Club owns data + code
Cost structure
Per-ticket fees on every sale
Predictable monthly fee · overage only above threshold
Individuality
Templated
Individualized per club via config & branding
Backend UX
Clunky · generic
Designed for club ticketing staff
Affordability
Too expensive for smaller clubs
Built for semi-pro & lower-tier budgets
AI features
Bolt-on, enterprise-only
Native — even for the smallest club
Scope
Ticketing only
Ticketing → CRM → newsletter → marketing
// Ambitions
Year 1 · mid-2027
10 clubs
First paying German clubs live across 3. Liga, Regionalliga, Oberliga.
Year 3
~100 clubs
German base saturated. First non-German clubs onboarded.
Long-term
All-in-one
The operating platform for clubs — ticketing, CRM, newsletter, marketing, access-control.

Three founders.
One unfair edge each.

Germany-based, co-located in Albershausen, going full-time on close. All three came up through the German dual-study system — work + study in parallel — so they bring unusually deep practical experience for their age.

CEO
Sebastian
Lives the customer's pain daily.

Currently runs ticketing, with hands-on responsibility across finance, communication, and cross-department coordination. Sees what every club pays incumbents — every day. Goes full-time on close of funding.

Owns Product · finance · domain credibility
Background German dual-study program
CTO
Adam
Engineer turned platform builder.

Bachelor's in technical engineering; completing a Master's via dual study. Owns the technical architecture: React + TypeScript, Python bridge to Rust, MongoDB Atlas on AWS Frankfurt, hybrid model-agnostic AI layer.

Owns Engineering · AI architecture
Background Technical engineering · dual study
CSO
Felix
The conversation opener.

Bachelor's in business studies (graduating 2026), dual-study background with practical commercial experience. Highly extroverted and built for the outbound role: federation contacts, club calls, in-person visits — leads the early sales motion until traction forces a hire.

Owns Outbound · sales · partnerships
Background Business studies · dual study
The common thread. All three founders went through the German dual-study system — work and study in parallel — which compresses years of professional experience into the early-twenties. Combine that with Sebastian's daily insider view of professional soccer ticketing and the team has both the customer empathy and the execution muscle to ship pilots before incumbents finish a planning meeting.