Cloud, Platform & AI Infrastructure Engineering
TEDEAS works with engineering organizations that have outgrown ad-hoc infrastructure but do not want a big-firm engagement.
AWS · Google Cloud · Kubernetes · Terraform · CI/CD · MLOps · Observability · AI Infrastructure
Who does the workThe person you talk to in the first conversation is the person on the engagement.
A technical first conversationScope, constraints, and whether the work fits. No pitch deck.
You'll hear from an engineer within two business daysTEDEAS Consulting Inc. is a Canadian technology consulting company specializing in cloud, platform, reliability and AI infrastructure engineering.
Representative areas of engineering experience.
High-volume consumer technology platform
Designed and implemented Kubernetes-based infrastructure for machine-learning and generative-AI workloads — serving, routing, rollout control, and observability as one platform path.
Read the case studyMulti-account cloud engineering organization
Designed and automated a shared cloud baseline — identity, networking, Kubernetes, and reusable foundations — so application teams could build without owning the underlying stack.
Read the case studyFintech product engineering team
Built Terraform and CI/CD as the only path to create environments, so infrastructure changes were reviewable, repeatable, and owned by the team after handover.
Read the case studyPublic-facing production application, high request volume
Rebuilt how production was observed, shipped, and recovered — telemetry that can be acted on, safer releases, and a defined path when things fail.
Read the case studyWhat we do
TEDEAS works on the infrastructure layer that application, data and AI teams depend upon.
Build infrastructure for deploying, serving, evaluating and operating machine-learning and generative-AI workloads in production.
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Build secure, scalable foundations that allow engineering teams to move faster.
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Turn infrastructure and deployment processes into repeatable software.
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Help production systems become more observable, resilient and operationally manageable.
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AI infrastructure
AI experimentation and production AI are different engineering problems. TEDEAS helps organizations build the infrastructure required to move AI workloads from experimentation into dependable production environments.
Read about AI Platform & MLOpsDefined engineering initiatives with clear objectives, deliverables and milestones.
Cloud platform build · Kubernetes implementation · MLOps platform · Infrastructure automation · Migration · Modernization
A senior engineer embedded in an existing platform, cloud or AI team.
Technical architecture, platform strategy, design reviews and implementation guidance.
Retained engineering assistance covering infrastructure, reliability, automation and platform operations.
01
Infrastructure and operational processes should be repeatable and version-controlled.
02
Production engineering should account for failure, observability and recovery from the beginning.
03
Build reusable foundations that improve how engineering teams work.
04
Documentation, maintainability and knowledge transfer are part of successful engineering delivery.
Technology expertise supporting modern cloud and AI platforms.
TEDEAS works with engineering organizations that have outgrown ad-hoc infrastructure but do not want a big-firm engagement.
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