A comparison of two approaches: sending 100,000 support tickets to a frontier AI that returns text ($151) versus TypeSafe Jev, a model that cannot return text ($2.11). Jev takes the context directly, making decisions at a fraction of the cost.
$151 vs $2.11 same 100,000 support tickets. same three decisions on every one of them the $151 version sends all of that to a frontier AI model and parses whatever text comes back the $2.11 version asks a model that cannot return text at all typesafe's jev takes your context
The author shares a Jev usage insight: it seemed tricky at first, but clicked when framed as 'not letting the AI write text.' Using inquiry handling as an example, the conventional approach dumps everything on GPT or Claude to analyze, while Jev takes a different approach.
The prototype uses AI to estimate hesitation or interest from cursor back-and-forth movements or lingering before clicking, observing customer demeanor and supplementing explanations or suggesting comparisons.
The author built an e-commerce real-time customer service demo using Jev and gpt-live-1, which instantly suggests recommended products based on the user's speech during a conversation, delivering a great experience.
