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Customer Story

Cybersecurity Software Company Accelerates Incident Resolution with AI Powered by a Contextual Data Layer

20% MTTR reduction goal

for targeted support workflows

Reduced support escalations

through AI-guided troubleshooting

40k+ daily AI requests

scalable AI-driven support operations at enterprise scale

Overview

The company is a leading provider of cloud-delivered cybersecurity solutions that enable organizations to securely connect users, devices, and applications while reducing reliance on traditional network infrastructure. Its platform focuses on securing internet and application access, reducing cyber risk, simplifying security operations, and improving user experience through a globally distributed cloud service.

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The Challenge

A leader in cybersecurity software wanted to improve how support engineers resolve complex customer issues across a rapidly growing enterprise environment.

Critical operational knowledge was spread across multiple disconnected systems, including Salesforce, Snowflake, Confluence, ServiceNow, internal documentation, and historical support cases. Engineers often had to manually navigate runbooks, prior incidents, customer configurations, and troubleshooting records to identify the next best resolution step.

The company initially deployed a vector-based retrieval system for AI workflows. While the system could retrieve relevant content, it struggled to connect relationships between incidents, runbooks, telemetry, and historical resolutions.

The result was a highly manual support process with:

  • slow navigation through complex troubleshooting workflows
  • inconsistent retrieval quality
  • limited visibility across related incidents and resolutions
  • reactive support engagement
  • difficulty preserving institutional knowledge across teams

Support resolution time became a major operational priority, with targeted workflows averaging nearly eight days to resolve and a goal to reduce Mean Time to Resolution (MTTR) by 20%.

The Solution

The company built an AI-powered “Account Context Graph” using the Arango AI Data Platform.

Arango enables the company to unify structured and unstructured enterprise data into a single contextual data layer that preserves how incidents, runbooks, customer environments, configurations, and historical resolutions are connected.

By combining graph, vector, document, key value, and search capabilities in one platform, the company can preserve relationships between:

  • support cases and related incidents
  • troubleshooting runbooks and resolution paths
  • customer configurations and telemetry
  • historical fixes and escalation patterns
  • enterprise knowledge sources across teams

This unified, trusted business context allows AI systems and support engineers to navigate complex operational workflows with more accuracy, speed, and explainability.

The company now supports more than 40,000 daily AI-driven support requests using this contextual data architecture while delivering faster, more reliable resolution guidance at enterprise scale.

Our vision is to build an evergreen, 360-degree business context layer for every customer—bringing together data, relationships, and operational signals so we can drive decisions across support, sales, and success.

This isn’t something you can achieve with isolated approaches to data, metadata, or embeddings. It requires a unified architecture that can model relationships, work across diverse data, and operationalize that context in real time—that’s the foundation for making AI truly effective at scale.

— VP of Data & AI at Cybersecurity Software Company

AI-Powered Support Experience

Using Arango, the company built an AI-powered digital teammate designed to guide support engineers through complex incident resolution workflows.

The digital teammate combines GraphRAG and VectorRAG to help engineers:

  • identify missing troubleshooting information
  • surface related incidents and historical fixes
  • navigate resolution runbooks
  • recommend next-best actions
  • accelerate issue resolution

By connecting enterprise knowledge into a unified contextual data layer, the company enables AI systems to understand relationships across incidents, telemetry, documentation, and operational workflows rather than simply retrieving isolated documents.

As we moved from retrieval-based chatbots to actionable AI agents, we realized that vector search alone wasn’t sufficient for support workflows that depend on step-by-step resolution and complete, accurate guidance.

We needed a contextual data layer that connects cases, incidents, runbooks, and knowledge sources—enabling our AI agents to understand context and surface the next best resolution step, reducing time to resolution while improving accuracy and trust.

— Senior Director of AI/ML Engineering at Cybersecurity Software Company

Explainable AI for Clinical Research

In enterprise support environments, recommendations must be accurate, trustworthy, and explainable.

The Arango AI Data Platform supports explainable AI by providing:

  • grounded reasoning behind recommended actions
  • evidence drawn from historical incidents and runbooks
  • visibility into related systems and dependencies
  • more transparent troubleshooting workflows

This allows support teams to trust AI-assisted recommendations while improving operational consistency and reducing manual investigation effort.

Results

Without Arango

  • Support knowledge fragmented across systems
  • Manual troubleshooting and resolution workflows
  • Inconsistent AI retrieval accuracy
  • High support escalation rates
  • Slow incident resolution times

With Arango

  • Faster AI-guided troubleshooting
  • Reduced support escalations
  • Increased support engineer productivity
  • Improved MTTR and CSAT
  • Faster access to related incidents and proven fixes
  • Scalable AI-driven customer operations

Why Arango

The Arango Contextual Data Platform enables organizations to connect fragmented enterprise data across siloed systems while preserving relationships between incidents, workflows, systems, and operational knowledge.

By combining graph relationships, vector embeddings, documents, and search in a single multimodel platform, Arango provides the contextual data foundation needed to power explainable AI and enterprise-scale operational intelligence.

With this unified, trusted context, the company improved support efficiency, reduced escalations, increased engineer productivity, and created the foundation for AI-driven customer operations at scale.

Accelerate incident resolution with Arango

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