Case study
Game data scraper and concept-risk prediction
A provider needed evidence before committing a full development cycle to a new slot concept.
The bottleneck
Validating a new slot concept was expensive because evidence arrived too late. The team needed a way to compare new ideas with actual market behaviour before committing the full development cycle.
What CONSYSTEAM built
A data pipeline that collects game-performance signals across providers, paired with a model that scores new concepts against historical patterns and risk indicators.
The purpose is not to replace creative judgment. It gives producers an earlier evidence layer for deciding where a team should invest its development time.
Delivery scope
- Automated market-data collection pipeline
- Normalised data model for analysis across sources
- Concept-level risk scoring workflow
- Internal dashboard and technical documentation
Result
The provider can evaluate weaker concepts before a full production commitment, creating a clearer basis for prioritisation and reducing avoidable development cycles.
