The MemX app successfully replaced FastText and a multi-stage Gemini pipeline with a single Jev call, achieving approximately 98% accuracy. Explore the full experiment and insights in the linked video.
added by @atripat
Looking to build with Jev or another Decisions API? For MemX app, I replaced the FastText and multi-stage Gemini pipeline with a single Jev call with ~98% accuracy.
See the full experiment and learnings in this video
An experiment integrating Jev into a SaaS AI Agent shows remarkable speed, responding faster than user input. It operates at approximately 100 times lower cost than traditional LLMs and efficiently directs requests needing a full LLM.
TypeSafe AI's Jev model has been integrated into a system layer, providing clean, typed decisions from unstructured state in under 500ms. This integration enhances backend processes by scoring and filtering context efficiently.
The integration of Jev from @typesafe_ai replaced three steps in Prio, achieving 100% accuracy in model routing and significantly reducing action review time from 5 seconds to 0.25 seconds for clear cases.
The argument validator in PatchOpsAi, built with Luna, shows performance metrics of 1.1–2.8 seconds, while @typesafeai's Jev took 195–251 ms on the validator and 221–901 ms on the scanner.