Case Study
A financial institution based in Australia operated a hybrid environment split across two clouds, each shaped by a different team — Azure running monolithic applications on virtual machines, AWS running microservices on Kubernetes. That configuration, built by separate teams, left an open question: did the entire infrastructure meet best practices for security, performance, and resource optimization?
The assessment covered network architecture, IAM practices, encryption at rest and in transit, patch management, instance sizing, and Kubernetes and serverless function configuration.
The deliverable was a prioritized report: findings ranked by risk, a cost-benefit analysis for each solution, and a phased roadmap designed not to interrupt ongoing operations.
Separation of the development and production environments into distinct AWS accounts, to reduce the risk of a change in one environment affecting the other.
Internal and external DNS split into separate hosted zones, to organize name resolution and reinforce the isolation between internal and public traffic.
A phased roadmap to upgrade Kubernetes and resize the virtual machines, designed not to interrupt ongoing operations.
Reserved instances proposed to reduce the infrastructure's recurring cost, based on the cost-benefit analysis from the assessment.
Nearshore means hiring a tech team in a country geographically and culturally close to yours — typically within 1–3 time zones. For US companies, that means Latin America. You get real-time collaboration, overlapping work hours, and engineers who operate in English, without the communication friction that comes with offshore teams 10+ hours away.
We're based in Argentina — UTC-3. That means 4–5 hours ahead of the US West Coast and 2 hours ahead of the East Coast. In practice, we maintain a daily overlap of 4–6 hours with most US-based teams, which covers standups, code reviews, and real-time problem-solving without anyone working at midnight.
Our work sits at the intersection of cloud infrastructure, application architecture, and AI. Concretely: cloud-native architecture design, infrastructure automation, platform engineering, data pipelines, and GenAI integration. We don't do generic cloud support — we build and run systems that need to scale.
Our primary depth is in AWS. We also work with Azure and GCP depending on the client's existing stack — the goal is always to work within your environment, not to push a preferred vendor.
Yes — and it's one of the problems we work on most. App modernization usually means one or more of the following: breaking a monolith into services, re-platforming to cloud-native infrastructure, replacing outdated dependencies, or improving the CI/CD pipeline so your team can ship faster. We start with a technical assessment before recommending any approach.
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