Your team is adding AI to a workflow that touches customer data, employee records, or sensitive business context. Clariva controls what reaches the model provider before the request runs.
A strong first use case has repeatable inputs, identifiable sensitive data, a defined AI workflow, and clear review pressure from security, privacy, platform, or customer-side reviewers.
| Workflow | Sensitive content that may appear | What Clariva changes | Customer value |
|---|---|---|---|
| Support ticket summarization | Names, contact details, account references, billing context, screenshots. | Transforms sensitive fields before provider execution. Blocks the request if proof or policy checks fail. | Lets support AI be tested with a clearer provider boundary and evidence trail. |
| Internal AI assistants | Employee messages, customer context, strategy notes, confidential documents. | Records the rejection reason and provider-route decision. Routes only approved requests to an allowed provider. | Gives security and platform teams a repeatable control path for internal AI use. |
| CRM / customer notes | Customer names, account context, pricing details, renewal risk, commercial strategy. | Transforms sensitive fields before provider execution and records the provider-route decision. | Supports AI-assisted sales and CX analysis with less uncontrolled provider exposure. |
| Embedded SaaS AI features | Workspace activity, private notes, tenant identifiers, user-generated content. | Routes only approved requests to an allowed provider and records blocked-request reasons. | Helps SaaS teams give enterprise reviewers clearer evidence for AI features. |
A focused evaluation works best when the workflow is specific and the data boundary matters.
Support tickets can include names, contact details, account identifiers, complaints, billing context, screenshots, and other sensitive information. Clariva helps control what reaches AI summarization.
Workflow policy, proof status, provider-route eligibility, and whether the decision should be admitted or rejected.
Missing proof, malformed request shape, route mismatch, or stale and reused proof can stop provider execution.
A support ticket and screenshot may include a customer name, email address, account reference, invoice number, payment reference, billing ZIP, and urgency context before the AI summary runs.
Before provider execution, account references, invoice numbers, payment references, email addresses, and billing ZIP codes can be transformed into policy-controlled placeholders. If proof or policy checks fail, the request is rejected instead of sent to the provider.
Internal assistants can touch strategy notes, employee messages, customer context, and confidential documents. Clariva helps control the request before model execution.
Policy fit, proof status, replay freshness, and whether the assistant request can use an eligible provider route.
Missing or malformed proof, stale challenges, reused challenges, or invalidated proof can block execution.
A document assistant prompt may include project names, employee names, customer account identifiers, pricing assumptions, and renewal-risk details before the model request is sent.
Employee, customer, project, and commercial fields are transformed or generalized according to the internal-assistant policy before model execution is allowed.
CRM notes often include customer names, account context, objections, pricing, renewal risk, and commercial strategy. Clariva helps reduce uncontrolled exposure before AI analysis.
Policy handling, provider-route eligibility, proof status, and expected vs actual outcomes for the CRM request.
Provider-route mismatch, malformed payload, missing proof, or failed proof verification can prevent provider execution.
An account review may include customer names, contacts, pricing pressure, commercial amounts, competitor references, and renewal-risk notes before AI analysis.
Customer identity, renewal-risk notes, commercial amounts, and competitor references can be generalized or removed according to the CRM workflow policy before the AI analysis route is admitted.
For multi-tenant SaaS products, Clariva can help evaluate how tenant-specific notes, workspace content, user identifiers, and private customer context are transformed, blocked, routed, or recorded before provider execution.
Request shape, proof status, runtime readiness, route eligibility, and scoped audit evidence for the product workflow.
Malformed payload, missing runtime configuration, provider-route mismatch, or replayed proof can stop provider execution.
An embedded AI request may include workspace identifiers, user references, task details, and private notes before the product routes the request to a provider.
Tenant, workspace, user, and private-note fields can be transformed before an embedded product feature sends provider-bound text to an AI model.
Some sensitive text workflows require stronger evidence that required controls occurred before model execution.
Scoped workflow policy, proof status, replay freshness, provider execution status, and whether outcomes match defined test scenarios.
Missing proof, tampered proof, stale challenge, reused challenge, or invalidated proof can block the request before provider execution.
A customer communication may contain names, payment issues, health-related context, dates, phone numbers, and other details before an AI summary is requested.
Policy can require stronger handling for payment, health-related context, and contact details, with failed requests rejected before provider execution.
Your team has a workflow where AI assistance is being tested, built, or approved.
The workflow may include customer, account, employee, operational, or regulated text.
Security, privacy, platform, or customer-side reviewers need reviewable proof before rollout.
Sensitive AI workflows often involve privacy, security, customer contracts, or internal AI governance policies. Clariva helps create request-path evidence for review, while your customer-side reviewers remain responsible for deciding which obligations apply.
Use the sample request path to see how an AI-bound workflow becomes an admitted or rejected decision with reviewable evidence.
See a sample request + decision