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
Customer data blocks rollout
Enterprise buyers want AI features, but their contracts, tickets, files, and PII cannot become visible to the SaaS operator or model provider.
02
Security reviews need proof
SOC 2 and procurement teams need attestable runtime evidence, no-log behavior, and a clear trust boundary before approving production AI.
03
AI must stay product-fast
Customers expect normal SaaS latency and developer ergonomics, not a bespoke self-hosted model project for every enterprise account.
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
|
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.
Enterprise support copilots
Route customer tickets, logs, and account history through private inference so support teams can use AI without exposing tenant data.
Private document analysis
Let customers summarize contracts, policies, and internal docs while prompts and retrieved context stay inside a measured runtime.
Customer analytics without PII exposure
Run segmentation, churn, and workflow recommendations with a signed proof path that security teams can inspect.
report
Enterprise AI SaaS
Founder welcome video
16 sec
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