ZERORE / INDUSTRY / GOV & ENTERPRISE
VERTICAL · 03
Regulated AI needs
proof, not promises.
监管场景的 AI 需要
证据，不是承诺。
Government and enterprise AI deployments operate under strict compliance requirements. ZERORE builds the evaluation infrastructure that turns "compliant in theory" into "compliant in production." 政府和企业级 AI 部署受到严格合规要求约束。ZERORE 构建评测基础设施，将"理论上合规"转化为"生产环境中合规"。
The challenge行业挑战
Compliance gaps become front-page stories. 合规漏洞变成头版新闻。
For regulated AI deployments, a single violation isn't a bug report — it's a regulatory inquiry, a contract termination, or a public incident. The cost of failure here is existential. 对于受监管的 AI 部署，一次违规不是 bug 报告——而是监管调查、合同终止，或公开事件。这里的失败代价是存续层面的。
No continuous compliance evidence 缺乏持续合规证据
Point-in-time audits miss what happens in production day-to-day. Regulators and enterprise customers increasingly require ongoing, automated proof that your AI behaves within policy — not just at certification time. 时点审计无法覆盖日常生产中发生的事。监管机构和企业客户越来越需要持续的、自动化的证明，而不仅仅是认证时刻的合规快照。
Data residency & privacy exposure 数据合规与隐私风险
Enterprise AI often processes sensitive data. Without strict controls on what the model sees, logs, or routes externally, you risk GDPR, PIPL, or sector-specific data-residency violations — silently, at scale. 企业级 AI 通常处理敏感数据。若缺乏对模型可见内容、日志记录与外部路由的严格管控，你将面临 GDPR、个人信息保护法或行业专项数据合规违规——在规模化场景中悄然发生。
Harmful content at enterprise scale 企业规模的有害内容
LLM outputs that are biased, discriminatory, or policy-violating can affect millions of users before detection. The latency between production failure and discovery is the risk window — and it's usually too long. 有偏见、歧视性或违反政策的大语言模型输出，可能在被检测到之前已影响数百万用户。生产失效到发现之间的延迟就是风险窗口——而这个窗口通常太长了。
How ZERORE helpsZERORE 如何解决
Compliance built into
every production decision.
将合规内嵌于
每一个生产决策。
Continuous content safety certification 持续内容安全认证
Automated, always-on detection of harmful, biased, or policy-violating outputs across all production traffic — with timestamped evidence logs that satisfy regulatory audit requirements. 对全量生产流量进行自动化、常态化的有害/偏见/违规输出检测，生成带时间戳的证据日志，满足监管审计要求。
Data residency & privacy compliance 数据合规与隐私边界
Monitor and enforce data boundary policies in real time — detecting PII leakage, cross-border data flows, and model input/output policy violations before they become reportable incidents. 实时监控并执行数据边界策略——在个人信息泄漏、跨境数据流动及模型输入/输出违规演变为须上报事件之前加以拦截。
Regulatory alignment reports 监管对齐报告
Generate structured compliance reports mapped to specific regulations — GDPR, PIPL, AI Act, CAC guidelines. Give your legal and procurement teams the documentation they need, automatically. 生成与特定法规对齐的结构化合规报告——GDPR、个人信息保护法、欧盟 AI 法案、国家互联网信息办公室指引。自动为法务与采购团队提供所需文档。
Pre-deployment quality gate 上线前质量门控
Run every model update through your compliance regression suite before it reaches production. Catch regressions in safety, bias, and policy adherence — before they cost you a contract. 每次模型更新在上线前均通过合规回归测试套件审查。在损失合同之前，提前捕捉安全性、偏见与政策遵从度的回归。
Start the conversation开启对话
Ready to make your
AI provably compliant?
准备好让你的
AI 合规有据可查了吗？
Tell us about your compliance requirements and we'll map them to a concrete evaluation and reporting plan. 告诉我们你的合规要求，我们将为你制定具体的评测与报告方案。
Reach out to联系
Roger Yang · Founder
roger@zerore.ai