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Workflow & Automation · usepeppr.ai

Peppr AI

Graded against 811 verified platforms, from its own policy text. Automated assessment against a published rubric — not legal advice.

Overall riskUNRATEDReviewed 2026-07-20
Benchmark

No lens is bandable yet — banding requires fully verified documents with on-criteria findings. The gap is shown honestly, never estimated.

0 verified findings0 policy surfaces1/1 core docs verified

Partially verified: Privacy Policy assessed. Everything below comes only from what was read in full.

Risk triage

No verified risks yet

AIRIN has not published verified findings for this record yet. The page shows the gap instead of guessing.

0
high
0
medium
0
low
1/1
docs
Trains on your data?
Not yet assessed
no verified finding covers this surface yet
Who owns outputs?
Not yet assessed
no verified finding covers this surface yet
Commercial use?
Not yet assessed
no verified finding covers this surface yet
Every rating:verbatim-citedsnapshot-datedchange-loggedHow we keep ratings honest →

How to read this page: Overall risk rates what Peppr AI's own policy terms mean for your prompts, outputs, and data. The benchmark bands below grade those same verified terms relative to peers — a platform in a risky-by-default category can rate HIGH risk and still grade STRONG against its peer set. Both trace to the cited findings.

Partially verifiedWorkflow & Automation

Partially verified — Privacy Policy — Verified (read in full, 0 findings). Findings below are from fully-read, verified documents only; remaining core documents are pending capture.

Why partial?

Terms not yet captured

AIRIN has not yet captured a gate-verified Terms of Service document for this platform.

Document status
  • Privacy Policy
    Verified - read in full - 0 citationsLast captured 2026-07-20
No verified evidence citations are published for this platform yet - its complete governing documents are not yet publicly capturable. We never publish citations from a document we have not read in full.

Clause intelligence

Canonical clauses and stance patterns extracted from the same gate-verified citations shown on this page.

13
clauses
4
patterns
4
stances
privacy sharing · 3training use · 1
privacy sharingHIGHPrivacy Policy › “Meet legal, security, and compliance obligations; and”

The clause permits sale of personal data or information.

Prevent fraud, abuse, and misuse. We do not sell personal data, and we do not use Customer content or Customer-identifiable usage data to serve third-party advertising.
Open source citation
privacy sharingMEDIUMPrivacy Policy › “5 . Model processing, and how your content is (and isn’t) used”

The clause permits disclosure or sharing with third parties, affiliates, vendors, or subprocessors.

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 Cu...
Open source citation
privacy sharingMEDIUMPrivacy Policy › “7 . Who we share information with”

The clause permits disclosure or sharing with third parties, affiliates, vendors, or subprocessors.

We share information only as needed to run the Service: Subprocessors - vetted vendors that provide infrastructure, transcription, model/inference, analytics, and support services on our behalf, under contractual data-protection obligations. A current list of subprocessors is available on request at support@usepeppr.ai. Within a Customer’s organization - the Service surfaces information to Users consistent with th...
Open source citation
training useLOWPrivacy Policy › “5 . Model processing, and how your content is (and isn’t) used”

The clause says submitted content is not used for model training or model/service improvement.

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 Cu...
Open source citation

Tier matrix

Plan-level conditions detected from citation-backed clauses. Empty tiers mean AIRIN has not captured decisive tier language yet.

TierSurfaceVerdictRiskCitations
All applicable tiersprivacy data useworsensHIGH1
All applicable tierstraining useconditionalMEDIUM2
Team / Businessprivacy data useconditionalMEDIUM1

Policy evolution

Open full timeline

Before/after stance changes across captured policy versions. When no material delta exists yet, AIRIN shows the latest citation-backed stance events instead.

Jul 20, 2026data sharingHIGH

Latest stance: sale or sell on privacy data use

Prevent fraud, abuse, and misuse. We do not sell personal data, and we do not use Customer content or Customer-identifiable usage data to serve third-party advertising.
Open timeline citation
Jul 20, 2026data sharingMEDIUM

Latest stance: third party or vendor sharing on training use

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 timeline citation
Jul 20, 2026data sharingMEDIUM

Latest stance: third party or vendor sharing on privacy data use

We share information only as needed to run the Service: Subprocessors - vetted vendors that provide infrastructure, transcription, model/inference, analytics, and support services on our behalf, under contractual data-protection obligations. A current list of subprocessors is available on request at support@usepeppr.ai. Within a Customer’s organization - the Service surfaces information to Users consistent with the access controls the Customer configures; a User sees only what they are entitled to see. Legal and safety - where required by law or legal process, or to protect the rights, safety, and security of Peppr, our Customers, or others. Business transfers - in connection with a merger, acquisition, or sale of assets, subject to this policy. We do not share Customer content or Customer-identifiable usage data with third parties for their own purposes.
Open timeline citation
Jul 20, 2026model trainingLOW

Latest stance: no training claim on training use

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 timeline citation

Capture recency

  • Privacy Policy:Last captured 2026-07-20· verified 2026-07-20verified once — not yet re-verified

Dates state when our pipeline captured and verified each document — not when the vendor last changed it. Documents are re-scanned on a recurring cadence; a document verified once says so until a re-scan confirms it again.

13 findings first captured First scan: July 2026.

Claim this profile

Compare and stack are saved in your browser. Open compare · View your stack. A correction triggers an automated re-read of Peppr AI's policies — no human edits the data.

Need this for procurement or legal diligence?

Free shows today's risk. A Stack Audit gives you a citable, verbatim-sourced PDF across your whole AI stack — and flags the moment a vendor's terms change.

Know where the missing document lives?

We haven't yet verified Peppr AI's Terms of Service. Point us at the official page and our pipeline will attempt to capture and read it in full. Submissions are candidates only — nothing is published until it passes the same verification gates as every other document on this site.

Every finding above is a verbatim quote from Peppr AI's own published policy, captured to an immutable snapshot and read in full through a two-gate verification pipeline. Confidence labels and any analysis are AI-generated and informational only — not legal advice.

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