Frequently Asked Questions

TietAI is designed for regulated health data from the start. The platform supports AES-256 encryption at rest, TLS 1.3 in transit, role-based access control, multi-factor authentication, tamper-evident audit trails, encrypted backups, and Business Associate Agreement workflows for US deployments. We also support security reviews, third-party audits, penetration testing, and SOC 2 Type II evidence during procurement and implementation.

Tenant isolation is enforced across the application, data, network, and access-control layers. Customer data is encrypted, access is scoped to the organization, and audit logs capture activity for compliance review. For organizations that require stricter boundaries, private-cloud and dedicated deployment models are available.

Implementation starts with discovery and architecture planning, then moves into platform deployment, EHR or source-system integration, pipeline configuration, testing, security validation, and go-live support. We work with your IT and security teams throughout, with implementation leads and technical architects assigned to the project. Timelines depend on scope and environment, but the aim is to get one critical use case live first and expand additional workflows in later phases.

Yes. We can assess existing Mirth channels, preserve the important routing and transformation logic, and move them into governed TietAI pipelines. Simple channels can often be migrated quickly; complex JavaScript transforms or site-specific edge cases may need technical review and validation before cutover.

TietAI supports common EHR and healthcare integration patterns, including HL7 v2.x, FHIR R4/R5, X12 EDI, DICOM, openEHR, REST APIs, files, databases, and message queues. We work with major systems such as Epic, Oracle Cerner, Dedalus, Allscripts, and athenahealth, and can add custom connectors when a deployment needs a local, legacy, or specialized interface.

TietAI can support on-premises, private-cloud, hybrid, and managed-cloud deployments for organizations with strict data residency, network, or procurement requirements. On-premises deployments typically use containerized infrastructure such as Docker or Kubernetes, with infrastructure provisioning, monitoring, backup, disaster recovery, upgrade responsibilities, and support boundaries agreed during implementation planning.

Pricing depends on the deployment model, connected systems, data volume, required modules, support level, and whether the environment is managed, private cloud, hybrid, or on-premises. We map pricing to the operational scope instead of charging for every individual API call, so teams can plan around predictable usage.

Support is matched to the criticality of the deployment. Production healthcare environments can include priority support, named success contacts, technical architecture support, training, and operational reviews. We agree response expectations and escalation paths before go-live.

SLA terms depend on deployment model, support package, and operational responsibilities. Managed environments can include automated failover, regional redundancy, monitoring, incident escalation, maintenance windows, disaster-recovery planning, status communications, and access to on-call engineering for critical production incidents. We define the exact uptime target and response process in the service agreement.

Yes. TietAI can expose data through FHIR APIs, SQL-friendly views, files, standard healthcare formats, and integrations with cloud data warehouses. Analytics teams can use cleaned, governed data for dashboards, reporting, cohort analysis, machine learning, and operational monitoring.