Jev is a constrained decision model that handles common classification tasks while honestly admitting uncertainty, making it an effective gatekeeper before larger language models. When paired with Gemini as a fallback for low-confidence cases, it matched Grok 4.6's 89.6% accuracy while being 6.24x faster and 8.7x cheaper, with Gemini needed for only 14.6% of questions.