Predicting demand before the weather decides for you.

For a company whose sales are directly tied to weather conditions, the company’s forecasting process was surprisingly manual. Sales teams gathered weather data city by city, cross-referenced it with historical sales, and relied heavily on experience to anticipate demand spikes or drops.

The problem wasn’t a lack of data, it was a lack of structure. Weather signals existed, but they were scattered, unprocessed, and disconnected from sales decision-making. There was no systematic way to detect an incoming cold snap in Montreal while managing inventory for Vancouver and Calgary at the same time. Outliers, whether a freak storm or an unseasonably warm October, were caught too late, after the damage was done.

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. What was once a manual, fragmented process became a single, consistent source of truth.

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.