Beetested utilizes artificial intelligence and affective computing to analyze player emotions and interactions in real time, providing development studios with actionable insights to enhance game design. This technology enables studios to make data-driven decisions that improve player engagement and reduce the risks associated with game launches.
Funding
$150K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Game development studios often rely on subjective feedback and limited playtesting data, making it difficult to quantitatively assess player engagement and identify potential design flaws before launch. Understanding player emotions and reactions during gameplay is challenging, hindering data-driven decisions to improve user experience and reduce the risk of unsuccessful game releases.
Solution
Beetested provides an AI-powered platform that analyzes player emotions and interactions in real-time, offering game development studios actionable insights to optimize game design and improve player engagement. The platform uses affective computing, sentiment analysis, and computer vision to capture and interpret player reactions through video, audio, and voice during gameplay. This analysis helps studios understand how players connect emotionally with the game, identify moments of frustration or disengagement, and make informed decisions to refine game mechanics, pacing, and overall user experience. Beetested offers a customized dashboard that aggregates data on target audiences, in-game behavior, and socio-demographic player analysis, enabling studios to make data-driven improvements and reduce the risks associated with game launches.
Target Audience
Beetested primarily targets game development studios, publishers, and investors who seek quantitative data to inform game design decisions, reduce launch risks, and objectify subjective elements of the game development process.
Features
- AI-driven analysis of player emotions and reactions using video, audio, and voice data
- Real-time sentiment analysis to identify moments of player frustration or excitement
- In-game behavior analysis to track player interactions and engagement patterns
- Socio-demographic player analysis to understand the target audience
- Customizable dashboard with aggregated data on player behavior and emotions
- Crowdtesting with target players to gather diverse feedback
- Generation of extensive customized PDF reports