Built for customer data that deserves careful handling.
Quantesic helps post-sale teams work with support history, account risk, sentiment, renewals, and customer context. Our security framework is designed around least-privilege access, tenant separation, human-reviewed AI, and practical procurement review.
The control model customers should expect.
Quantesic follows a straightforward security framework: identify who is acting, limit what they can access, protect customer data in transit and at rest, review sensitive AI output, and maintain auditable operational practices.
Access starts with verified identity.
Users sign in through supported authentication flows, then receive access based on workspace membership and role. Administrative functions are reserved for authorized customer administrators and Quantesic support operators.
Permissions are checked before protected actions.
Quantesic separates ordinary workflow access from sensitive operations such as user administration, integration setup, AI provider configuration, and customer support activity.
Customer workspaces stay logically separated.
Customer records are organized by workspace and protected by application-layer and data-layer controls that prevent users from accessing another customer's account context.
Customer data is protected across the service.
Traffic uses encrypted transport. Stored data is handled through managed infrastructure, with direct customer-facing data mutation paths limited to controlled application workflows.
Support access is limited and purposeful.
Quantesic administrative access is reserved for support, implementation, tenant management, and reliability work. Customer-visible support and trust activity is designed to make administrative intervention easier to review.
Changes are reviewed through build and deployment checks.
Product changes go through source control and production build validation. Customer-impacting controls are documented and reviewed as the platform evolves.
Customer data should not become someone else's shortcut.
Quantesic uses customer context to support that customer's workflows. The product is not designed to turn one customer's private support history into another customer's advantage.
No cross-tenant model training.
Customer data is not used to train cross-tenant models or foundation models. Tenant-specific feedback is intended to improve that tenant's experience.
Human review before action.
AI output is designed to be inspectable and editable before it affects customers, escalations, renewal narratives, or account communication.
Source-aware context.
Quantesic presents synthesized context with attention to the underlying customer systems and records that informed the work.
Provider choice for enterprise teams.
For customers with stricter AI governance needs, Quantesic supports review of provider routing, usage visibility, and enterprise key-management options during implementation.
How the service is designed at a safe level of detail.
This overview gives security and procurement teams enough architecture context to evaluate the system without publishing sensitive implementation details, internal paths, or operational runbooks.
Every workspace action is tied to a signed-in user.
Protected workflows require an authenticated user session. The application evaluates workspace membership and role before returning customer data or allowing a sensitive operation.
Customer records are scoped to the customer workspace.
Account records, tickets, activity, customer notes, AI usage, and integration status are handled as workspace-scoped data. Cross-workspace access is not part of the standard customer user model.
Connected systems are authorized deliberately.
Integrations use provider authorization flows and expose readiness state so administrators can distinguish configured, connected, and operationally active systems.
Sensitive credentials are handled outside normal customer records.
Provider keys, OAuth secrets, and enterprise AI keys are treated as sensitive configuration. Quantesic avoids displaying raw secrets back to users after setup.
Important actions are designed to leave reviewable evidence.
Administrative changes, support actions, AI review flows, and customer-impacting workflow events are designed to create an operational trail that can support customer review and internal investigation.
Operational readiness is part of the implementation plan.
Quantesic reviews availability expectations, support contacts, escalation paths, data handling obligations, and customer-specific requirements during rollout planning.
What we can cover during diligence.
Architecture review
Workspace boundaries, data flow, integration model, administrative access model, and AI processing posture.
Access and role review
Supported sign-in patterns, administrator responsibilities, user roles, support access, and offboarding expectations.
Data handling review
Customer data categories, retention expectations, deletion and export handling, integration scopes, and privacy obligations.
AI governance review
Human review model, provider routing, tenant-level usage visibility, customer key options, and customer-specific AI restrictions.
Contractual review
Security exhibits, confidentiality commitments, data processing terms, subprocessor review, and incident notification terms.
Implementation review
Source systems, rollout scope, admin setup, least-privilege configuration, and mutually agreed support procedures.
Need the detailed Trust Pack — sub-processors, DPA, data flow, breach SLA? Request via email
Clear answers without exposing the blueprint.
We keep public answers high-signal and safe. Detailed evidence, architecture discussion, and customer-specific control mapping can be handled directly during procurement.
Can Quantesic complete a security questionnaire?
Yes. We can complete customer security questionnaires and provide supporting explanations appropriate to the review stage and confidentiality requirements.
How is customer data separated?
Customer data is scoped by workspace and governed by authenticated access, role-aware permissions, and application controls that prevent standard users from crossing workspace boundaries.
Does Quantesic train shared models on customer data?
No. Customer data is not used to train cross-tenant models or foundation models.
Can customers request export or deletion?
Yes. Export, deletion, and retention expectations are handled through the customer agreement and implementation process.
How should we review integrations?
We review each connected system, requested authorization scope, operational readiness state, and customer responsibility before rollout.
Do you publish detailed infrastructure diagrams publicly?
No. We share appropriate architecture detail with customers during a controlled security review instead of publishing sensitive implementation information on the public website.
Bring the diligence questions before rollout.
We can walk through access control, tenant separation, integrations, AI handling, data retention, and the evidence your security team needs for a customer-specific review.