Korean AI Firm SelectStar Releases Model Routing Tech 'Davi', Claims 28% Cost Cut in Self-Tests
SelectStar launched Davi, an AI decision model that routes queries based on complexity. Internal tests show it reduced inference costs by 28% while slightly improving accuracy compared to using only top-tier models.
South Korean AI reliability firm SelectStar announced on October 26 the release of 'Davi', a specialized AI decision model designed for 'model routing'. This technology analyzes the difficulty of incoming queries and directs simple questions to lightweight AI models while assigning complex problems to high-performance ones, aiming to balance cost and accuracy.
According to internal test data published by SelectStar, Davi outperformed 'Zev', a similar decision AI developed by TypeSafeAI, across several benchmarks. On the 231-question 'ZevBench', Davi achieved an accuracy of 89.2%, surpassing Zev's 86.6%. In tasks requiring judgment of complex conditions, Davi scored 77.5%, which was 4.5 percentage points higher than Zev.
In a specific cost-efficiency experiment involving 174 high-difficulty Korean Q&A samples repeated five times, SelectStar found that when Davi automatically selected between Anthropic's Haiku, Sonnet, and Opus models, the overall accuracy reached 71.1%. This was slightly higher than the 70.3% accuracy achieved by always using the highest-performance Opus model alone. Simultaneously, this dynamic routing approach reduced model execution costs by approximately 28%.
It is important to note that these results are entirely self-reported by SelectStar. The company acknowledged that the experiments were limited to high-difficulty Korean scenarios and that the reported cost savings did not include the overhead of calling the Davi router itself. Consequently, the net cost efficiency in actual enterprise environments remains to be verified. Additionally, SelectStar has open-sourced a model routing plugin under the Apache 2.0 license for developers to test similar functionalities.