Cloud and DevOps: the engine behind the AI.

Kubernetes, CI/CD, reliability engineering and cost optimization. We build and run the infrastructure that keeps AI products fast, stable and affordable to operate.

In numbers

Faster, steadier, cheaper to run.

70%

Faster deployments

Kubernetes migration
99.95%

Uptime target for critical services

SRE engagement
50–55%

Lower monthly cloud cost

Hybrid cloud optimization
Four engagements

What we did and how it works.

Each engagement below is from our own work. Alongside the result, we explain the approach in plain English.

70%

Kubernetes migration

We moved a monolith to microservices on AWS EKS. Releases now ship with zero downtime, the platform has 3× the capacity, and deployments are 70% faster.

Splitting a monolith into services lets each part be deployed, scaled and rolled back on its own. Kubernetes replaces running services gradually during a release, which is what makes zero-downtime deploys possible.

~80%

Faster CI/CD

Faster GitHub Actions pipelines and a self-healing ECS runtime for streaming.

CI/CD is the path from a code change to production. When it is quick, teams ship small changes often. A self-healing runtime replaces failed tasks automatically, so a streaming service recovers without waiting for someone to step in.

99.95%

SRE engagement

A 99.95% uptime target for critical services, backed by reliability targets, unified monitoring and runbooks. About 60% faster recovery.

A reliability target turns “keep it up” into a number a team can plan around. Unified monitoring puts every signal in one place. Runbooks mean the response to a known failure is written down before it happens.

50–55%

Hybrid cloud

Lower monthly infrastructure cost for a high-traffic VPN, while maintaining uptime.

A hybrid setup runs each workload where it makes the most sense, instead of paying cloud prices for everything by default. Cost goes down without trading away reliability.

Why it matters for AI

AI is only as good as where it runs.

Models, agents and detection systems depend on the platform underneath. Slow deploys delay every improvement. Weak monitoring hides failures. An oversized cloud bill can make a good product too expensive to run.

From the model to the cloud it runs on, we design, build and operate the whole system, so the AI and its infrastructure are planned together. The same thinking goes into the AI agents we build and into Epoch AI, our fraud detection platform, which can be deployed on-premises.

How we work

Understand first, then build.

01

Understand

We learn your business, systems and processes first, so we solve the real problem.

02

Solve

A creative team working with AI designs the solution that fits how you operate.

03

Evolve

We map how your systems can advance with AI and automation, step by step.

04

Grow

Virtual Internet began as a client. Today we build Epoch AI together.

More about how we partner

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