OpEra Technologies

Companies running factories, warehouses and fleets make daily operational decisions, like which orders go on which vehicle across several depots, or how to allocate orders in a marketplace, that are complex enough to require mathematical optimization but are often handled without it. The page cites examples: a pet retailer's delivery routing, a services marketplace's order allocation, and a sugar producer's sales-plan optimization, implying these decisions were previously made without the kind of modelling OpEra provides. When plans don't account for real-world constraints, the result shown in OpEra's case studies is higher delivery costs, excess idle time, repressed demand, and lower margin per tonne.

OpEra describes itself as an AI decision engine that ingests a company's data, maps how the operation actually works, and builds a custom mathematical model for a specific decision the client wants support with, such as routing, scheduling, or allocation. The model runs against the live state of the operation, and when reality breaks the plan, users can add a constraint in plain language rather than rebuilding the model. OpEra's stated process has five stages: a one-week learning phase, a one-week backtest proof against historical data, a four-week pilot proof run live beside the client's team, a two-week integration into the client's existing stack, and ongoing monitoring and improvement of the custom model.