Project Delivery
Defined engineering initiatives with clear objectives, deliverables and milestones.
Cloud platform build · Kubernetes implementation · MLOps platform · Infrastructure automation · Migration · Modernization
Cloud, Platform & AI Infrastructure Engineering
Infrastructure for Modern Software and AI
TEDEAS Consulting helps engineering organizations design, automate and operate reliable cloud, platform and AI infrastructure. From Kubernetes and infrastructure-as-code to MLOps and production AI systems, we help teams move from architecture to dependable production operations.
AWS · Google Cloud · Kubernetes · Terraform · CI/CD · MLOps · Observability · AI Infrastructure
What we do
Engineering the platform underneath the product
TEDEAS works on the infrastructure layer that application, data and AI teams depend upon.
AI Platform & MLOps
Build infrastructure for deploying, serving, evaluating and operating machine-learning and generative-AI workloads in production.
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Cloud & Platform Engineering
Build secure, scalable foundations that allow engineering teams to move faster.
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DevOps & Infrastructure Automation
Turn infrastructure and deployment processes into repeatable software.
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Reliability & Production Engineering
Help production systems become more observable, resilient and operationally manageable.
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AI infrastructure
Put AI into production — reliably.
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.
- 01Applications / AI Products
- 02AI Gateway / Serving
- 03Models / Inference Infrastructure
- 04Kubernetes / Compute / GPU
- 05Cloud Platform
- 06Observability / Security / Automation
How We Work
Embedded Engineering
Senior engineering capability embedded within an existing platform, cloud or AI team.
Advisory & Architecture
Technical architecture, platform strategy, design reviews and implementation guidance.
Ongoing Platform Support
Retained engineering assistance covering infrastructure, reliability, automation and platform operations.
Built for production
01
Automate by Default
Infrastructure and operational processes should be repeatable and version-controlled.
02
Reliability Matters
Production engineering should account for failure, observability and recovery from the beginning.
03
Platforms Over One-Off Solutions
Build reusable capabilities that improve how engineering teams work.
04
Handover Matters
Documentation, maintainability and knowledge transfer are part of successful engineering delivery.
Selected capabilities
Business capability first. These are the areas we work in — not a vendor partnership wall.
Cloud
- AWS
- Google Cloud Platform
Containers & Platforms
- Kubernetes
- Docker
- EKS
- GKE
Infrastructure
- Terraform
- Infrastructure as Code
- Networking
- IAM
Delivery
- CI/CD
- Deployment automation
- Release engineering
- Git-based workflows
AI Infrastructure
- MLOps
- Model serving
- LLM infrastructure
- GPU workloads
- AI gateways
- Production inference
Reliability
- Monitoring
- Logging
- Metrics
- Alerting
- Observability
- Production hardening
Selected Engineering Work
Categories of engineering experience — not attributed case studies.
Cloud Platform Engineering
Design and implementation of production cloud environments including networking, identity, Kubernetes, automation and operational tooling.
Production AI Infrastructure
Engineering infrastructure for deploying and operating machine-learning and generative-AI workloads using Kubernetes-based platforms.
Infrastructure Automation
Terraform-based infrastructure, automated deployment workflows and reusable platform components.
Reliability Engineering
Observability, monitoring, deployment safety and operational hardening for production systems.
Need experienced engineering capability?
Talk to TEDEAS about your cloud, platform, reliability or AI infrastructure initiative.
Discuss a Project