Cloud and Data Infrastructure for an Automation Platform

Cloud architecture, CI/CD, data pipelines, and observability for an AI-driven workflow automation product.

The challenge

What the client was facing

A fast-growing automation SaaS was outgrowing its initial cloud setup — costs spiraling, deploys getting risky, AI workloads unpredictable, and observability essentially absent.

What we built

The solution

Re-architected the cloud stack on Kubernetes with proper namespacing and quotas
Hardened CI/CD: previews, blue-green deploys, automated rollback
Data pipeline rewrite for AI feature inputs with backfill capability
Full observability stack: metrics, logs, traces, cost visibility per tenant

In production

What it looks like

Illustrative screens — actual client UI, branding, and data redacted under NDA.

AI-first delivery angle

Why AI-first mattered here

Senior cloud engineers built the foundation so AI features could ship safely and predictably. AI workload autoscaling, cost monitoring, and model-version rollout were all first-class concerns.

Technologies

Stack

AWS Kubernetes Terraform ArgoCD Datadog Kafka

Have a similar problem to solve?

Book a free 30-minute call with a senior engineer. We’ll tell you honestly whether AI-first delivery is the right fit — and what a realistic engagement would look like.

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