Case Study
A company with a large engineering team began equipping its resources with AI and rolling out AI agents in its development process. A few months in, leadership had no reliable information to tell whether the initiative was on track, what to adjust, or whether it had been a mistake. Decisions rested on perception and scattered anecdotes, not evidence. What isn't measured doesn't improve.
A financial services company with a large engineering team began equipping its resources with AI and rolling out AI agents in its development process. A few months in, it needed to apply the same rigor it applies to any other investment: measure, don't assume.
We designed and built a dashboard that consolidates information from multiple sources — AI agent usage logs, Git repositories, project management tools like Jira, and CI/CD platforms — to replace perception with evidence. The dashboard covers four fronts: adoption (which teams and developers use the agents, and how often), productivity (cycle time, agent-assisted PRs, delivery velocity), quality and risk (bugs introduced vs. avoided, test coverage, review rate), and cost and usage patterns (consumption by team, tokens, cost per PR or per feature delivered).
With the dashboard in production, the team moved from discussing AI adoption based on impressions to doing so with concrete, up-to-date data.
Decisions were made quickly, no longer stalled by viewpoints that bordered on lacking the evidence to back them up.
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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