ZERORE / INDUSTRY / AI COMPANIONSHIP
VERTICAL · 01
Where AI meets humanity —
quality is everything.
AI 与人类相遇之处——
质量即一切。
Emotional and efficiency AI products live or die by a single interaction. ZERORE's eval infrastructure catches failure patterns before users do — keeping trust intact at scale. 情感与效率兼顾的 AI 产品，往往因一次错误交互而功亏一篑。ZERORE 的评测基础设施在用户察觉之前捕捉失效模式，在规模化场景中守住信任。
The challenge行业挑战
Companionship AI fails silently. AI 陪伴产品悄无声息地失效。
Users don't file tickets — they ghost. By the time retention metrics drop, the trust damage is already done. 用户不会提工单——他们直接流失。当留存数据开始下滑时，信任损伤早已无法挽回。
Silent quality failure 无声的质量失效
Companionship agents fail in ways users can't articulate. Tone drift, context collapse, safety edge-cases — none of these surface in standard logs. You're flying blind. 陪伴类 Agent 的失效方式难以言明：语气漂移、上下文崩塌、安全边缘案例——这些在标准日志中几乎不可见，团队如同蒙眼驾驶。
Irreversible trust erosion 不可逆的信任损耗
One wrong response in an emotional context — a harmful suggestion, a misread mood, a broken persona — can sever a relationship the user spent months building. 情感语境中一次错误回应——一个有害建议、一次情绪误判、一次人格崩塌——就能切断用户用数月积累的关系。
No regression visibility 零回归可见性
Every model update risks re-introducing past failures. Without a living regression set built from real sessions, you can't know what you broke — until users leave. 每次模型迭代都可能重现历史失效。没有基于真实会话构建的动态回归测试集，你无从知晓破坏了什么——直到用户离开。
How ZERORE helpsZERORE 如何解决
Eval infrastructure built for
emotional AI at scale.
专为规模化情感 AI
打造的评测基础设施。
Session quality scoring 会话质量评分
Automatically score every production session on tone, safety, persona-consistency, and goal alignment — surfacing the worst interactions for human review, not randomly sampled ones. 对每一条生产会话自动评分，覆盖语气、安全性、人格一致性与目标对齐——将最差交互而非随机抽样推送至人工审核。
Tone & safety evaluation 语气与安全评测
Detect emotional tone drift, harmful outputs, and safety boundary violations across millions of turns — with explainable labels, not just flags. 跨百万轮次检测情感语气漂移、有害输出与安全边界违规——输出可解释标签，而非简单的标记。
Failure pattern intelligence 失效模式识别
Cluster production failures by root cause — not just symptom. Turn recurring failure patterns into structured test cases that survive model upgrades. 按根因而非表象对生产失效进行聚类分析，将反复出现的失效模式转化为可在模型升级中存续的结构化测试用例。
Living regression set 动态回归测试集
Every resolved failure automatically seeds your offline eval suite — so the regression set grows with your product and catches recurrences before they reach users. 每个已解决的失效自动注入离线评测套件——回归测试集随产品成长，在失效重现前将其拦截。
Start the conversation开启对话
Ready to evaluate
your AI companion?
准备好评测
你的 AI 陪伴产品了吗？
Tell us about your product and we'll show you exactly where your quality gaps are — and how to close them. 告诉我们你的产品情况，我们将精准定位质量缺口，并给出闭环方案。
Reach out to联系
Roger Yang · Founder
roger@zerore.ai