Peppr AI policy evolution
Before/after stance changes across captured policy versions, with exact citations. If no before/after delta is available yet, AIRIN shows the latest citation-backed stance events instead.
No before/after stance delta is available for this filter yet. Latest citation-backed stance events are shown below.
Jul 20, 2026trainingmedium
data sharing
Latest stance: third party or vendor sharing
“Peppr uses AI models to transcribe conversations, detect questions, and generate answers grounded in a Customer’s connected knowledge. What this involves - and where the data goes - depends on the deployment model, so we describe each directly. No cross-customer training. We do not use one Customer’s content, transcripts, or AI outputs to train, tune, or improve any model or instance made available to any other Customer. Each deployment is grounded only on the knowledge that Customer connects, for that Customer’s instance alone. In-environment / on-premise (BYOC) deployments. Customers connect Peppr to their own model endpoints - they bring their own models. Customer content and AI outputs never leave the Customer’s environment or reach Peppr-controlled infrastructure, so Peppr has no ability to retain, train on, or improve any model from them. The retention and data-use terms that govern inference are those of the Customer’s own agreement with the model provider it chooses to connect. Hosted (cloud) deployments. Transcription and answer generation are performed by subprocessors Peppr engages to operate the Service (see Section 7), under contractual data-protection terms. Retention and data-use terms depend on the provider and are set out in the applicable order form or data processing addendum. Customers with strict zero-retention requirements can meet them through an in-environment (BYOC) deployment.”
Open citationJul 20, 2026traininglow
model training
Latest stance: no training claim
“Peppr uses AI models to transcribe conversations, detect questions, and generate answers grounded in a Customer’s connected knowledge. What this involves - and where the data goes - depends on the deployment model, so we describe each directly. No cross-customer training. We do not use one Customer’s content, transcripts, or AI outputs to train, tune, or improve any model or instance made available to any other Customer. Each deployment is grounded only on the knowledge that Customer connects, for that Customer’s instance alone. In-environment / on-premise (BYOC) deployments. Customers connect Peppr to their own model endpoints - they bring their own models. Customer content and AI outputs never leave the Customer’s environment or reach Peppr-controlled infrastructure, so Peppr has no ability to retain, train on, or improve any model from them. The retention and data-use terms that govern inference are those of the Customer’s own agreement with the model provider it chooses to connect. Hosted (cloud) deployments. Transcription and answer generation are performed by subprocessors Peppr engages to operate the Service (see Section 7), under contractual data-protection terms. Retention and data-use terms depend on the provider and are set out in the applicable order form or data processing addendum. Customers with strict zero-retention requirements can meet them through an in-environment (BYOC) deployment.”
Open citationGenerated from live stance events. Informational only, not legal advice.