Case Study
A risk analysis platform for banking ran on Java/Angular on AWS, but the critical operations in an assessment's lifecycle — client onboarding, configuration, closing, report generation — depended on copying and pasting data between systems. In a business where that data feeds credit risk decisions, every manual step was a surface for error.
We designed an event-driven architecture on Amazon SNS: an internal topic orchestrates the critical operations, and independent handlers in Java execute them from structured JSON messages. This decouples business logic from manual intervention and leaves the door open to trigger these processes from other systems or on a schedule.
In parallel, we automated deployments with a multi-module build flow in Maven, single-source Docker image versioning, and automatic backups before every overwrite. We also contributed ongoing work on the data validation pipelines (Lambda + Fargate) and on the secure file exchange over SFTP with each client bank, automating the onboarding of users and cryptographic keys.
Processes that took hours of manual data entry between systems are now resolved in minutes.
A multi-module Maven build flow, with single-source Docker image versioning and automatic backups before every overwrite, eliminated unplanned downtime.
Decoupling business logic from manual intervention reduced the surface for error in the data feeding credit risk decisions.
Automating secure file exchange over SFTP and the onboarding of users and cryptographic keys sped up the incorporation of new client banks, without compromising security.
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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