Predicting demand before the weather decides for you.

The sales team had developed sharp instincts for reading weather-driven demand. They tracked conditions city by city, cross-referenced patterns with historical sales, and used that experience to anticipate spikes and drops. Their read on the market was often right.

But the data gathering couldn’t scale. Weather signals were scattered across sources, disconnected from sales tools, and impossible to monitor simultaneously across 10 cities. Detecting an incoming cold snap in Montreal while managing inventory for Vancouver and Calgary required more throughput than any team could sustain manually. Outliers, whether a freak storm or an unseasonably warm October, were caught too late to act on.

Rather than committing to a full platform, Mirego scoped the project as a focused PoC in two phases, designed to confirm that AI was the right tool for the client’s needs before investing further.

Phase 1 - Weather data gathering Mirego built an application on Forra that collects, structures, and visualizes weather data across up to 10 Canadian cities. Sales and operations teams can access historical, current, and forecast data through a clean interface, and export everything to a structured Excel document for sharing across teams. The team’s existing knowledge now sits on top of a single, always-current data layer.

Phase 2 - Automatic outlier detection Building on the foundation of Phase 1, the application was extended to actively identify anomalies. Based on upcoming weather forecasts and past sales cycles, the system flags events likely to affect demand, whether triggered by rules defined by the sales team or surfaced automatically from historical patterns. A monitoring module keeps teams informed in real time.