Guillermo Campoamor

Meep

Multimodal mobility platform connecting public and private transport into a single interface

Status: Built, scaled, and exited
Geography: Europe & LatAm (6 countries)
Timeframe: ~2017–2026

Why this problem

Urban transportation is inherently fragmented.

Each mode (public transit, ride-hailing, micro-mobility) operates in its own system, with its own incentives, interfaces, and data. For users, this translates into friction: too many apps, poor coordination, and limited visibility into real options.

At the same time, cities were pushing toward more sustainable mobility, but without a unified layer to make it usable.

User problem

  • Switching between multiple apps to complete a single journey
  • Limited digital access to public transportation in many cities
  • No seamless way to combine modes (e.g. bus + ride-hailing + scooter)
  • Inconsistent UX across operators

A simple example: you could not plan and execute a trip that combined public transit and Uber from a single interface.

Thesis

Mobility would shift from mode-centric to user-centric.

The winning layer would not be another operator, but a neutral aggregation platform that:

  • integrates all transport modes
  • provides real-time planning and booking
  • aligns with both cities and private operators

This would become the foundation for Mobility-as-a-Service (MaaS).

At the same time:

  • cities were digitizing infrastructure
  • APIs were becoming available
  • users were already conditioned by platforms like Uber

Market tailwinds were strong, with MaaS projected to grow rapidly driven by multimodal platforms and subscription models.

What I built

A B2B and B2G mobility platform that unified fragmented transportation systems into a single interface.

Core components:

  • Multimodal journey planning (based on OpenTripPlanner + custom layers)
  • Booking and payment across transport modes
  • White-label apps for cities and enterprises
  • Microservices infrastructure to integrate heterogeneous operators

The platform connected:

  • Public transit systems
  • Ride-hailing (e.g. Uber, Cabify)
  • Demand-responsive transport (DRT)
  • Micromobility providers
  • EV charging networks
  • Additional services (e.g. parking, tourism)

Evidence

  • Operated in 6 countries
  • 180+ integrations across transport and payment providers
  • Deployed with cities, transport authorities, and enterprises
  • Raised $12.7M across multiple funding rounds
  • Built and led a multidisciplinary team across product, engineering, and operations
  • Company acquired by Mobico (former National Express)

How I used AI / technology

The stack was not “AI-native” in today’s sense, but the challenge was deeply technical and data-driven.

  • Built multimodal routing and planning systems on top of OpenTripPlanner
  • Designed integration architecture to unify highly fragmented transport APIs
  • Developed scalable backend infrastructure to orchestrate real-time data across providers
  • Focused on interoperability and speed of integration as core advantages

On the data side, we incorporated early machine learning models to improve reliability and user experience:

  • Time-series forecasting (D Predict) to anticipate demand and system behavior (AWS SageMaker, Google Vertex)
  • Delay prediction models using gradient boosting techniques (e.g. Random Forest)
  • Passenger clustering to better understand usage patterns and segment behavior
  • NLP-based sentiment analysis to extract insights from user feedback and improve service quality

The goal was not just to aggregate mobility options, but to make the system predictable, adaptive, and increasingly personalized.

The core problem was less about algorithms and more about orchestrating complex systems reliably.

What I learned

  • The hardest part is not the technology, it’s aligning incentives across stakeholders
  • Public-private partnerships require as much political navigation as technical execution
  • Speed of integration becomes a competitive advantage in fragmented markets
  • UX matters more than coverage once a minimum threshold is reached
  • Selling into cities requires patience, credibility, and long-term commitment

Outcome

Meep scaled across multiple markets and was ultimately acquired by Mobico, where the platform became part of a broader global mobility strategy.