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Tech worldSeptember 4, 20262 min read

Cloud.ru automates part of its enterprise support workload

Cloud.ru reports that its assistant resolves 10% of requests automatically and accelerates complex-ticket diagnosis with an internal knowledge base.

A stream of requests passes through a metallic knowledge store towards an engineering console
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Cloud.ru says its internal AI assistant now resolves 10% of enterprise-customer requests without an engineer. For the remaining tickets, it classifies the request, gathers history, and suggests relevant knowledge-base material before handing the case to a specialist.

How the workflow operates

The assistant uses an internal library of more than 25,000 articles. It handles routine questions from documentation and prepares context for complex cases, including similar incidents, customer history, and diagnostic material. Customers retain the option to contact a person.

According to the company, information retrieval now takes between 30 seconds and three minutes instead of 15–20 minutes, while the time for a new engineer to begin independent work fell from six months to two or three. These are vendor-reported results from one implementation, not a universal benchmark for support teams.

Where the practical value lies

For B2B support, value is not limited to an automated answer. Faster context assembly can shorten the time to the first useful diagnostic step and reduce repetitive searching across separate systems.

The result depends on knowledge quality. Each article needs an owner, review date, applicability boundary, and service-version link. Without those controls, the assistant merely returns an outdated answer more quickly.

Metrics worth monitoring

  • tickets reopened after automated closure;
  • time to resolution rather than only first response;
  • hand-off rate and escalation reasons;
  • accuracy of service, customer, and configuration-version identification;
  • customer feedback on the answer;
  • incidents where automation delayed diagnosis.

The use case is practical because automation sits inside an existing support workflow and is bounded by a curated knowledge base. A longer quality trend and a comparison of request cohorts before and after deployment are still needed for an independent assessment.

Source: Cloud.ru’s official publication dated 4 September 2026.

Primary source: Cloud.ru: AI assistant for B2B support

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