AI Foundry for a mid-market industrial group
挑战
Pilot chatbots on public LLM APIs could not pass security review: no tenant isolation, no retrieval governance, unclear cost attribution, and no path from demo to production SLAs.
解决方案
cloudstrata deployed Managed AI Foundry on the customer’s preferred cloud: agent orchestration, RAG over internal knowledge bases, role-based access, secrets isolation, and usage metering shared with finance. cloudstrata operates the platform layer; the customer owns use cases and data.
结果
First governed agents in production within weeks. Shared cost and quality metrics for engineering and finance. A repeatable path to add domains without rebuilding platform plumbing.
客户利益
The organization runs production AI with procurement-grade controls—without staffing a dedicated platform team or accepting unmanaged SaaS sprawl.
Mid-market industrial groups often sit between hyperscaler ambition and lean IT capacity. Public LLM pilots create excitement, then stall at security, data residency, and cost questions. This engagement started with that gap: the customer needed agents that could answer from controlled document corpora, with auditability and predictable spend.
cloudstrata introduced AI Foundry as Managed AI Platform Engineering as a Service. The runtime provided multi-tenant isolation, retrieval pipelines with access controls, model routing, and usage metering. Inference and embeddings ran in EU-hosted infrastructure aligned with the customer’s cloud preference.
Delivery focused on one high-value domain first—technical documentation and service knowledge—then expanded with the same platform contracts. Evaluation harnesses and tracing made quality regressions visible before wider rollout.
IT and procurement received the controls they needed: identity integration, secrets handling, quota policies, and cost reports that matched engineering telemetry. Product teams kept ownership of prompts, corpora, and business outcomes.
The result is a durable operating model: cloudstrata runs the platform layer under SLA; the customer scales use cases. Names and proprietary details remain confidential at the customer’s request.
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