Enterprise AI SaaS with private inference.
Give enterprise customers AI features without exposing their prompts, documents, or customer data to your cloud operators or model providers.
tenant audit
01
客户数据阻碍发布
企业买家想要 AI 功能，但他们的合同、工单、文件和 PII 不能暴露给 SaaS 操作员或模型提供方。
02
安全审计需要证明
在批准生产环境 AI 之前，SOC 2 和采购团队需要可验证的运行时证据、无日志行为以及清晰的信任边界。
03
AI 必须保持产品速度
客户期待的是标准 SaaS 延迟和开发者体验，而不是每个企业账号都要定制一套自托管模型项目。
solution mockup
Private inference fits behind the SaaS API your customers already use.
Reusing the private inference mockup: application requests go through a measured CVM, model calls stay inside a no-log runtime, and each response can carry a receipt for security review.
# qbr-risk
QBR research with sealed customer context
Today
OpenClaw can read approved workspace sources through Phala CVM.
Maya Chen
9:41 AM
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zero trust log mesh
Enterprise sources sealed into one AI audit trail.
source
request
user / team
records
policy
status
GitHubSam Lee
Security
NotionMaya Chen
RevOps
GmailNora Patel
Success
JiraLuis Romero
Support
SnowflakeAnika Rao
Data
use cases from the report
Where AI SaaS needs confidential compute.
企业支持副驾
将客户工单、日志和账户历史通过隐私推理路由，让支持团队在不暴露租户数据的情况下使用 AI。
隐私文档分析
让客户在提示词和检索上下文都留在经过度量的运行时内时，摘要合同、政策和内部文档。
无需暴露 PII 的客户分析
运行分群、流失和工作流推荐，并附带安全团队可审查的签名证明路径。
report
