AI-Enablement for Zmanda Technical Support Teams

Enabling secure, AI-driven support workflows

This demonstrates how organizations can operationalize LLMs across complex product ecosystems without exposing sensitive data directly to AI models. By introducing a secure orchestration layer, enterprises can accelerate support workflows, reduce manual investigation effort, and maintain strong governance over data access.

The challenge

Supporting a data protection platform like Zmanda requires engineers to correlate information across multiple sources, including:

• Customer and tenant context
• Backup and restore job details
• Operational logs and failure patterns
• Support tickets and historical cases
• Product documentation and troubleshooting guides

Manually piecing together this information increases cognitive load and slows response times, especially as customer environments scale.

The solution: MCP-driven AI orchestration

We built a dedicated MCP server that acts as a secure orchestration layer between Zmanda’s internal systems and Large Language Models.


Instead of giving AI models direct access to APIs or sensitive data, the MCP server:

• Gathers only authorized, relevant context
• Structures and sanitizes the data
• Exposes controlled tools to LLMs
• Returns actionable insights to support engineers


This approach allows AI models to reason effectively while maintaining strict control over security and governance.

How MCP enables smarter LLM-based support

With the MCP server in place, LLMs can assist support teams by:

• Correlating support tickets with the correct customer environment
• Analyzing backup jobs, failures, partial runs, and skipped operations
• Identifying likely root causes based on operational signals
• Retrieving the most relevant documentation and troubleshooting steps
• Generating high-confidence draft responses for support engineers

Technical implementation details

• AI Platform: Claude integrated via custom MCP servers
• Architecture: MCP-based orchestration across support systems
• Integrations:
  • CRM systems (customer data, ticket history)
  • Product platform telemetry and configurations
  • Knowledge base and technical documentation
• Data Access: Real-time operational signals and logs

Business impact

• Reduced Tier 4 escalations by 60%
• A scalable AI foundation enabling 3× faster response times
• 50% faster log collection and analysis
• Improved support consistency