Case studies

Concrete results, measured in production.

A few representative engagements: the situation before, what was put in place, and what it delivered.

–40%

Before: Each team ran its own AI models. The server (GPU) bill grew every month and response times were uneven.
After: A shared platform routes each request to the cheapest model able to handle it, and scales automatically with demand.

vLLMKubernetesGPU

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Seconds

Before: Staff searched through dozens of documents and kept interrupting the same experts.
After: A secure internal AI assistant answers from company documents, cites its sources and has its quality measured continuously.

RAGLangChainpgvector

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÷3

Before: The team was drowning in alerts; real problems were buried in noise and dragged on.
After: Alerts are triaged, grouped per incident and prioritised automatically: 70% fewer useless alerts.

AIOpsPrometheusSRE

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Minutes

Before: Every release involved manual steps that differed from team to team, with room for error.
After: Standardised, automated paths with security and monitoring included by default.

BackstageArgo CDCrossplane

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Clients anonymised for confidentiality.

Ballpark

Adjust the figures to your situation. Assumption: AI takes over half the time spent on these repetitive tasks.

704 hrecovered per year

€31,680of working time per year

Indicative estimate (50% of the time automated, 44 working weeks). The intro call is where we size your real case.

Intro call · 20 min · free

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