Case Study: 80%+ Faster Issue Diagnosis for a Global Connectivity Platform

Industry: Connected Devices, IoT, Mobility
Company Type: Global SaaS connectivity provider
Primary Use Case: Technical Support Engineering & Incident Diagnosis

The Challenge

A global SaaS provider powering connectivity for connected vehicles, IoT devices, and point-of-sale systems was struggling with long investigation cycles for customer issues, directly impacting resolution times and customer satisfaction.

The company operates a highly distributed, carrier-dependent infrastructure where failures can originate from many layers of the stack. Support investigations required manual correlation across analytics platforms, operational databases, application and network logs, and product documentation, a common failure mode of systems that rely on tools instead of end-to-end operational workflows.

Engineers routinely fielded questions like:

"Why can't vehicles in one region connect while the same firmware works elsewhere?"

Answering this required reconstructing context across product versions, carrier behavior, configuration changes, and recent incidents—all scattered across different tools. Engineers spent hours just understanding what went wrong before remediation could even begin.

Prior AI Attempts Fell into the POC Trap

The team had already experimented with general-purpose LLM tools, workflow automation platforms, and cloud AI SDKs: the same approaches that often succeed in demos but struggle to reach reliable production use. These efforts ran into familiar problems associated with AI pilots that never reach operational scale:

• High internal effort — quarters of engineering time to build and maintain
• Integration gaps — difficulty reasoning across multiple production systems
• Unclear path to scale — proofs-of-concept that never became operational services

They needed a solution that could operate as part of real support workflows, not as a separate experimental tool.

The Solution

The company deployed a Squid AI technical support agent that runs continuously inside their existing ticketing environment, where engineers already work, following the principle that enterprise agents must operate inside existing workflows, not alongside them.

Rather than a simple chatbot, the agent performs actions and structured investigation. It connects to logs, databases, product manuals, and operational procedures, then correlates signals across systems to identify likely root causes and next investigative steps. It returns answers with explicit source references and investigation paths, allowing engineers to validate conclusions quickly and move directly to resolution.

Continuous learning built into operations. The agent improves over time by learning from prior cases and confirmed resolutions, incorporating explicit engineer feedback, and updating its reasoning as products, carriers, and configurations evolve — an example of how production agents learn safely over time rather than relying on static prompts or retraining cycles. This transforms support knowledge from static documentation into a continuously improving operational system.

Path to proactive detection. With diagnostics automated, the next phase extends the same system to identify emerging patterns across tickets and telemetry, detect issues earlier in the failure lifecycle, and trigger investigation before customers experience visible failures. The long-term goal is not just faster response, but fewer support cases altogether.

The Impact

80%+ faster mean time to resolution (MTTR)
50% reduction in escalations to senior engineering teams
Higher first-contact resolution rates
Improved customer satisfaction (NPS)

Operationally, teams also reported faster onboarding of new support engineers, more consistent investigation quality, reduced time spent per investigation by frontline engineers, and less fatigue from repetitive diagnostic work.

The system now operates as part of standard support infrastructure—not as an experimental AI tool.

Why It Worked

This deployment succeeded because it focused on augmenting real workflows rather than introducing parallel systems, consistent with principles behind designing agentic systems around workflows, not tools. The agent was embedded directly where work already happens, designed around investigation rather than just Q&A, and integrated across operational data sources with transparent, auditable reasoning paths.

Instead of starting with architecture or platforms, the company started with a concrete operational problem and deployed an agent designed to own that class of work end-to-end.

Applicable Use Cases

This pattern applies to organizations with complex, distributed systems and escalation-heavy operational workflows, including technical support engineering, incident response teams, network operations, and reliability engineering groups.

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