Selected client work
The useful part of a case study is not the logo. It is the constraint, the system that changed, and the evidence that the new platform could be operated by the team that owned it.
What stopped a valuable AI workload from becoming a secure, operated product.
What changed in the architecture, delivery path, controls, and operating model.
What the client could deploy, measure, govern, and own after the engagement.
Client engagements
We publish only work that can be described without exposing client systems or inflating the result. The engagements below show the decisions, implementation, and measurable operating outcomes.
How Drizzle built a production AI data platform processing one million daily events across self-hosted models, training pipelines, and dynamic LLM routing.
Read the full case studyHow Drizzle helped a senior engineering team take an AWS-first agentic cloud operations platform to production readiness in approximately two months.
Read the full case studyHow Drizzle designed and implemented a company-owned AI platform in AWS, giving five departments one governed path for around ten agent workloads at a projected 70 percent lower operating cost than the managed-service direction.
Read the full case studyHow we present the work
What stopped a valuable AI workload from becoming a secure, operated product.
What changed in the architecture, delivery path, controls, and operating model.
What the client could deploy, measure, govern, and own after the engagement.
Your production constraint
Bring the workload and the current system. We will help identify the constraint worth solving first.
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