Case Study
A fintech that assesses risk for banks was losing time and accuracy to a problem that's easy to underestimate: running its entire assessment lifecycle by hand. Creating an assessment, onboarding a bank, configuring the setup, and pulling BI reports meant copying and pasting data between systems, with no automated process in between. Backend and frontend deployments had no fixed workflow either, and when an inconsistency showed up in the risk calculations, nobody caught it until it had already affected something. As the company added more banks as clients, the process didn't just get slower — it got riskier.
Renaiss designed an event-driven architecture on AWS that automated the full risk assessment lifecycle, end to end. An internal Amazon SNS topic orchestrates the platform's critical operations, and Java handlers resolve every process that used to be manual — creating assessments, closing them, onboarding clients, setup — as soon as a message is published. This architecture decouples business logic from manual intervention and leaves the system ready to integrate with other systems down the line.
In parallel, backend and frontend deployments were automated with dedicated pipelines and a standardized Maven build, file validation was strengthened with Lambda and Fargate running independently of the main backend, and the inconsistencies in the risk calculation formulas were fixed, with control checks on every change. On the data-exchange side with the banks, the existing SFTP infrastructure was hardened and the creation of users and cryptographic keys was automated with Python.
Automation eliminated manual work between systems entirely. Processes that used to take up the team's time now run in minutes, with far less risk of an error reaching sensitive financial data. Deployments became predictable and stopped causing outages from human error. Risk data reliability improved steadily, thanks to validation that corrects proactively instead of waiting for something to break. And all of it was built without pausing operations: much of the work was done on a system already in production, with banks actively using it.
End-to-end automation replaced a process that was consuming the team's time and introducing errors at every step.
Automated pipelines eliminated the outages caused by human error in the deployment process.
Proactive validation fixes inconsistencies in risk calculations before they reach a business report.
Every operation is logged, meeting the audit standards the financial sector demands.
The system was upgraded while real banks were actively using it, without interrupting service at any point.
The event-driven architecture leaves the company ready to onboard more banks without rebuilding its technology foundation.
Discovery & Assessment
We audited the full risk assessment lifecycle, the manual deployments, and the existing validation processes to pinpoint where errors originated and what the new architecture needed to solve.
Event-Driven Architecture
We implemented an internal Amazon SNS topic that orchestrates the platform's critical operations, with Java handlers that run each process as soon as the corresponding message arrives.
Deployment Automation
We built backend and frontend pipelines on a multi-module Maven build that enforces a strict dependency order.
Data Reliability
We improved the file validation architecture and fixed the risk calculation formulas, verifying every change with control queries before and after applying it.
Secure Exchange & Integration
We hardened the existing SFTP infrastructure on AWS Transfer Family and automated new-bank onboarding with Python.
Dashboards & Handoff
We added key indicators to the BI dashboards built on Athena and QuickSight, and handed off a system the client's team can monitor and extend on its own.
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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