Specialist engineering for the infrastructure underneath modern software and AI systems.
Build infrastructure for deploying, serving, evaluating and operating machine-learning and generative-AI workloads in production.
AI experimentation and production AI are different engineering problems. Prototypes stall when serving, GPUs, rollout safety, and observability are treated as afterthoughts.
Build secure, scalable foundations that allow engineering teams to move faster.
Application teams slow down when the cloud account is a pile of one-off resources. Networking, identity, and Kubernetes have to be a platform — not a ticket queue.
Turn infrastructure and deployment processes into repeatable software.
Manual consoles and snowflake environments make change risky. Delivery should be version-controlled, reviewable, and the same in every environment.
Help production systems become more observable, resilient and operationally manageable.
Systems that only work on a happy path fail expensively. Reliability has to be designed in — telemetry, deploy safety, and a path to recover.
Talk to TEDEAS about your cloud, platform, reliability or AI infrastructure initiative.
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