Verification Over Assumption
Enterprise AI systems need evidence that required controls actually occurred.
Why Clariva
Sensitive AI trust starts before the model runs.
Clariva helps teams prove how sensitive AI requests were controlled, routed, or rejected before execution.
Founding Purpose
Why Clariva focuses on the request boundary.
Clariva was built for teams that need policy, routing, rejection, and review evidence in sensitive AI workflows.
Enterprise teams are adding AI to support, CRM, internal assistant, and product workflows faster than they can consistently review what reaches model providers. Clariva focuses on that boundary: the policy applied, the provider-bound payload, the admit-or-reject decision, and the evidence created for review.
The idea began while working on privacy-sensitive communication workflows. That work exposed a broader enterprise problem: sensitive text often moves into AI systems before teams have a clear control point, rejection path, or reviewable record.
Founder View
The problem is not only model behavior.
- Enterprise AI risk often starts before a model answers.
- The critical moment is what reaches the provider and under what policy.
- Security, privacy, and platform teams need evidence before trust.
- Clariva was built to make that control point explicit and reviewable.
CEO & Founder
Tolga Cengiz
Clariva is founded by Tolga Cengiz, an enterprise strategy and product leader with experience across AI-enabled product development, digital transformation, strategic partnerships, and large-scale commercial initiatives.
Before Clariva, Tolga worked on enterprise growth, innovation, and partnership initiatives involving organizations including Dell EMC, Samsung, Fossil Group, and Kelly Services. That background shaped Clariva’s business-first approach: the product is designed not only as technical infrastructure, but as a control layer that security, legal, compliance, platform, and business teams can evaluate together.
Clariva is currently an early-stage company focused on working directly with enterprise teams to define how sensitive AI workflows can be controlled, verified, and audited before execution.
Founder Background
Business-first AI control.
- Enterprise GTM, partnerships, and product strategy experience.
- Hands-on founder-led product development from concept to working prototypes.
- Experience translating emerging technology into enterprise adoption paths.
- Focus on privacy-first AI workflows and accountable execution boundaries.
Company Principles
The operating beliefs behind Clariva.
Clariva is built around a simple principle: sensitive AI workflows need enforceable controls, not only retrospective explanations.
Runtime Policy Enforcement
AI policy should be part of the operational request path, not only a written guideline.
Provider Flexibility with Central Control
Enterprises can use multiple providers without fragmenting governance across each route.
Evidence Matters
Security, compliance, and engineering teams need reviewable records to approve sensitive AI workflows.
Patent-Pending Architecture
Focused on verifiable privacy-preserving workflow control.
Clariva’s verification approach is patent pending.
Patent-pending status reflects the novelty of Clariva’s verification and request-control architecture; specific implementation details remain confidential.
Patent-pending status is not a granted patent or a legal guarantee.
Where Clariva Focuses
- The admission decision before sensitive workflow data reaches downstream model execution.
- The transformation step that determines what the provider can receive.
- The routing decision that determines which provider path is eligible.
- The rejection path when proof, policy, readiness, or routing requirements fail.
- The audit evidence that lets teams review what happened.
Evaluate the control model.
Review whether verification-first AI control fits one sensitive workflow in your environment.