The project delivered a clearer AI strategy, improved prototype performance with measurable quality gains, and equipped the client with practical methods and tools to confidently scale AI within their platform.
Meet our client
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Client’s Challenge
A leading provider of cloud-based infrastructure monitoring had developed a prototype AI assistant to summarize IT alerts, but the output quality was inconsistent and not production-ready. The company needed expert guidance to refine their approach, evaluate LLM options, and establish a clear strategy for scaling.
Our Solution
We conducted a 4-week AI advisory project, combining LLM expertise with hands-on engineering to improve the client’s prototype. Our team defined evaluation criteria, split summarization into subtasks, and introduced RAG with infrastructure topology and historical resolutions. We also optimized prompt engineering through live experiments, performed cost analyses of API-based vs. self-hosted LLMs, and recommended monitoring tools to support scalable LLMOps.
Client’s Benefits
The project delivered a clearer AI strategy, improved prototype performance with measurable quality gains, and equipped the client with practical methods and tools to confidently scale AI within their platform.
