Project Description
As Technical Product Manager during a startup challenge at innogy SE, I led the development of a Smart City MVP designed to optimize the placement and effectiveness of digital advertising screens through real-time pedestrian insights. The solution combined IoT and AI technologies, using infrared camera systems to anonymously detect and count pedestrian traffic patterns in urban environments.
Within a highly time-constrained and innovation-driven setting, I guided the product from initial concept to working prototype. This included defining the product vision, prioritizing core features, and aligning a cross-functional team around a clear MVP scope. I worked closely with engineers on the technical implementation of the computer vision model, while also ensuring that the solution addressed real business use cases such as location intelligence and data-driven decision-making for advertisers.
In parallel, I translated complex technical capabilities into a clear product narrative, making the solution understandable and compelling for stakeholders and evaluators.
Problem solved:
The project addressed the challenge of limited data transparency in physical environments, particularly for businesses relying on location-based decisions such as digital advertising placement. Traditionally, understanding pedestrian traffic and engagement in public spaces has been either manual, inconsistent, or lacking in real-time accuracy.
The opportunity was to create a scalable and privacy-conscious solution that provides reliable insights into pedestrian flow and density. By enabling more accurate measurement of foot traffic, the product helps businesses make better-informed decisions about where to position digital assets and how to optimize their reach and impact.
At the same time, the challenge was to balance technical feasibility, data accuracy, and privacy considerations within the constraints of an MVP.
Technologies used:
- AI-based computer vision models
- IoT integration
- Agile product development
- Prototyping and MVP validation frameworks
Outcome:
The MVP achieved 85% accuracy in pedestrian recognition, demonstrating the technical feasibility and real-world applicability of the solution. The prototype successfully validated the core concept of using AI-driven insights for location intelligence in urban environments.
The project was recognized with a financial award to support further development, highlighting both the innovation potential and business relevance of the idea. Beyond the technical outcome, the project showcased strong product leadership under pressure—rapidly moving from concept to prototype, aligning diverse stakeholders, and delivering a clear, value-driven solution within a limited timeframe.
Overall, the project illustrates the ability to bridge emerging technologies and real-world use cases, turning complex ideas into tangible, testable products.
