SLAtech Fitness
83/100Class-conflict-aware, RTL Hebrew polish, injury-flag routing native
Reproducible 200-question Fitness-specific eval harness. +15-point lift vs generic SLAtech-Business (68/100). Driven by конфликт записи на занятие detection, membership-tier upsell discipline, и trainer-matching by experience-level. Пара с umbrella eval scoreboard, Fitness glossary и Fitness FAQ.
| Category | Fitness-tuned | Generic | Lift |
|---|---|---|---|
| Class-booking conflict detection Member's existing class roster проверяется перед новой booking — нет двойное бронирование 6pm yoga vs spin slot. Generic chatbots happily over-book. |
88 | 62 | +26 |
| Membership-tier upsell discipline Bot quotes member's current tier privileges + upgrade-path price clearly, без aggressive cross-sell pressure. Generic chatbots over-pitch upgrades на every interaction. |
86 | 66 | +20 |
| Trainer-matching by experience-level Beginner / intermediate / advanced classification против trainer specialty + member history. Generic chatbots match по next-available-slot only. |
84 | 71 | +13 |
| Injury / medical-flag handling Если member discloses recent injury, bot routes к coach review перед booking high-intensity class. Generic chatbots book regardless of injury context. |
81 | 58 | +23 |
| Multilingual class info (HE / RU) Hebrew RTL polish на class titles, Russian transliteration of trainer names, locale-aware time formatting. |
79 | 76 | +3 |
Class-conflict-aware, RTL Hebrew polish, injury-flag routing native
Strong native scheduling но weaker injury-flag handling и English-first
Нет class-schema awareness, нет injury-flag, English-first
Нет class schema, нет Hebrew RTL, conversation cap на lower tiers
Per-vertical eval score — один input. Три других инструмента самообслуживания закрывают картину без звонка с продавцами:
Eval methodology — open-source. 200 sealed Fitness-specific questions с LLM-as-Judge scoring на factuality, hallucination и confidence axes.