Inside the Case
Problem
Manual property matching was subjective, slow, and impossible to scale effectively.
Solution
- Designed an ML ranking pipeline (based on location, price, and user preferences).
- Architected the system blueprint: data collection → ML matching → analytical dashboard.
- Defined the scalable technology stack for the backend and DevOps infrastructure.
- Established the Roadmap, budget, MVP timeline, and team structure.
Result
- Prepared a complete architectural package for the grant application.
- Delivered a transparent technical and cost model.
- The project is fully ready for a pilot launch in the Czech Republic.
ML RankingETL PipelinesTerraformCI/CD
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