Case Study
A corporate transport company runs more than 80 routes a day, and each one needs its own driver and vehicle assignment. For a long time, planning that operation day to day depended entirely on a single person able to hold all of that complexity in their head, working from an Excel spreadsheet with no formulas or validations. Any mistake turned directly into a service failure, and the whole operation was at risk whenever that person was out — with no room to delegate or to scale the number of routes.
Renaiss surveyed and documented every rule that governs the scheduling, classifying them by priority — which ones are non-negotiable and which are preferences that can give way when needed — so the system could automate without losing the human judgment behind the process.
Building on those rules, we developed a web application that centralizes routes, vehicles, and drivers, prioritizes assignment continuity, and learns those patterns through use. With AI assistance, the full schedule for more than 80 daily routes is now generated with a single click. The application also includes dashboards by client, driver, and vehicle, a resource management panel for leave, vacations, and maintenance, and a Google Maps integration that calculates each route's distance automatically.
The impact comes down to two concrete indicators: time and dependence on a single person. What used to take one person's entire workday now takes less than 20 minutes. And the knowledge that used to live only in that person's head is now built into the system as validation rules: today, three different people can run the scheduling without errors. Together, these changes turned a critical, fragile process into one that is fast, reliable, and that the whole team can sustain.
Scheduling more than 80 daily routes went from taking an entire workday to being done in under 20 minutes.
Knowledge that used to depend on a single person is now built into the system: today three different people can run it without errors.
Every rule that used to exist only in one person's experience was surveyed, classified, and turned into a system validation.
The system learns through use which driver goes with which vehicle and route, with no repeated manual configuration.
Dashboards by client, driver, and vehicle give the client a view of its own operation that didn't exist before.
A panel for leave, vacations, and maintenance makes assignments efficient and avoids overtime costs.
Business Rules Discovery
We documented every rule that governs daily scheduling and classified them by priority, separating the non-negotiable ones from those that can give way when needed.
Web Application Design
We built an application that centralizes routes, vehicles, and drivers, with assignment continuity as a core design requirement.
Learning Assignment Patterns
We implemented the logic for the system to learn through use which driver belongs to which route and vehicle, with no repeated manual configuration.
AI-Assisted Automation
We built the validation rules into the scheduling engine, so the full assignment of more than 80 routes is generated with a single click.
Dashboards and Resource Management
We added metrics by client, driver, and vehicle, plus a panel to manage leave, vacations, and fleet maintenance.
Google Maps Integration
We connected the application to the Google Maps API to calculate the distance of each route automatically.
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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