FLYR’s pricing engine, powered by decision intelligence takes a forward-thinking approach that goes beyond traditional pricing tools. Instead of simply supporting decisions, we make them. By analyzing real-time data and adapting automatically, the engine allows you to optimize pricing with minimal manual effort, enabling faster decisions and better outcomes.

At the heart of FLYR’s platform is a philosophy of intelligent automation. Rather than relying on human-set rules or static strategies, the engine continuously learns from the data it receives. This approach can be compared to a self-driving car: once it’s set in motion, it navigates on its own, responding to real-world conditions in real time. That doesn't mean you can't influence it - more on that later! This shift from reactive to proactive pricing helps you stay ahead of demand changes, often before they’re visible to the human eye.

FLYR's pricing engine is designed to be data-smart. The main data source is the transactional booking data coming from your PMS. Together with other data sources such as market and events data, the engine evaluates the relevance and reliability dynamically.

While the engine does consider historical trends, it puts a strong emphasis on future-facing data. This allows it to respond quickly to changing conditions such as shifts in booking patterns, external events, or fluctuations in demand. Updates to forecasting models happen hourly, ensuring that pricing decisions are always grounded in the most current information available.

Machine learning is the engine’s brain. It maps out booking likelihoods for every room and rate combination, at every point in the booking window. These models are highly tailored and update automatically based on new inputs.

Reinforcement learning allows the system to adjust its strategies based on real-time feedback. For example, if a price point performs better or worse than expected, the engine adapts accordingly, improving with every hour and every booking. This creates a micro-targeted pricing strategy that is unique to each situation and continually refined.

Thanks to its granular design, FLYR’s engine can make precise pricing decisions for every room type and rate code. It forecasts demand up to two years into the future, adjusting for availability changes, events, and other variables. You don’t have to micro-manage. Instead, the system takes care of the heavy lifting, and you step in only when needed—what we call “management by exception.”

This model also allows for full automation in situations where there’s no historical data to fall back on, such as with newly opened hotels or during unusual market conditions. It adapts to capacity changes automatically and maintains its accuracy regardless of operational shifts.

Although the engine is designed to operate autonomously, it also incorporates your manual inputs. If you apply a price override, the system will take that into account. It considers how far the override deviates from the original recommendation, the direction of the change, and where in the booking window it occurs. This flexibility ensures that your strategic decisions are reflected, while still benefiting from the AI’s continuous learning. More about how to work together and influence FLYR's AI pricing can be found here.

Altogether, FLYR's optimisation engine gives your revenue team more time to focus on strategic initiatives. With pricing largely automated and continuously optimized, it should give you back the gift of time to direct your attention toward strategic decision making, improving business mix, refining your distribution channels, enhancing content and merchandising, or fostering a revenue-first culture across your organization.