Enterprise AI

Tensor9 for Enterprise AI

Enterprise AI vendors develop sophisticated platforms that enhance workflows and productivity through intelligent search, predictive insights, and automated processes. However, enterprise customers often have stringent data governance policies, require high levels of security, and demand operational resilience. Additionally, enterprise AI workloads frequently involve accessing sensitive, siloed data across multiple systems, which makes centralized SaaS deployments challenging.

Tensor9 enables enterprise AI vendors to overcome these challenges by delivering their software directly into customer environments without sacrificing the cloud-native experience.

Key Challenges for Enterprise AI Vendors

1. Data Privacy and Sovereignty:
   Enterprise customers in regulated industries (e.g., finance, healthcare, government) often mandate that sensitive data, such as customer information or proprietary documents, remain within their environment to comply with privacy laws and regulations.

2. Heavy and Siloed Datasets:
   Enterprise AI platforms typically need to aggregate and process large amounts of data from multiple internal systems (e.g., document repositories, communication tools, financial records). Centralizing this data in the vendor’s cloud can be costly, slow, and non-compliant.

3. Customer-Controlled Access:
   Enterprise customers often require control over the level of access vendors have to their systems and data. This can limit the ability of vendors to provide real-time support or telemetry.

4. Operational Resilience:
   Customers require continuous availability of AI-powered tools, even during vendor-side outages, DDoS attacks, or regional cloud disruptions.

How Tensor9 Helps

Tensor9 empowers enterprise AI vendors to address these challenges by enabling Any-Prem deployments that deliver software directly into the customer's cloud, on-prem, or air-gapped environment.

1. Data Privacy and Compliance:
   Tensor9 ensures that enterprise AI platforms are deployed within customer-controlled environments, preventing sensitive data from leaving their infrastructure. This supports compliance with regulations such as GDPR, HIPAA, and internal security mandates.

2. Local Data Aggregation and Processing:
   Tensor9 allows AI platforms to connect to data sources and process information locally within the customer’s environment. This avoids data egress fees and ensures compliance with internal policies around data residency.

3. Customer-Controlled Change Management:
   Tensor9 gives customers full control over the timing and approval of software updates, ensuring updates are applied on their terms and in line with their operational needs.

4. Observability and Secure Support:
   Tensor9 provides vendors with audit-logged telemetry and supervised support access. This allows vendors to monitor performance metrics, debug issues, and deliver support without accessing customer data directly.

5. Resilient Deployments:
   Tensor9 enables customers to deploy AI platforms with localized failover and high availability configurations. This ensures uninterrupted service even if the vendor’s infrastructure experiences downtime.

Example Scenario: Enterprise Work AI Platform

An enterprise AI vendor provides a platform that improves knowledge discovery and internal workflows by indexing files, emails, and collaboration tools to offer predictive search and recommendations. The platform must access data from multiple internal systems, including sensitive communications and documents.

Traditional SaaS Challenges:

• The enterprise cannot move internal documents and communications to a vendor’s cloud due to the sensitivity of the data.
• A SaaS-based model would introduce the risk of downtime if the vendor’s infrastructure were to go offline.

Tensor9 Solution:

• The vendor's platform is deployed directly within the customer’s private cloud or on-prem infrastructure, ensuring that all data remains within the customer's control.
• The vendor can provide observability and secure support through audit-logged telemetry and supervised access.
• The customer configures localized redundancy and failover to ensure the platform remains available even during external network outages.

Why This Matters

For enterprise AI vendors, Tensor9 unlocks access to regulated and security-sensitive markets by enabling customers to deploy AI solutions within their own environments. By supporting Any-Prem deployments, Tensor9 helps vendors meet enterprise demands for data control, security, and resilience while preserving the cloud-native experience.