FLYR Hospitality (Optimize, Insights, Planning, Groups)

par FLYR Hospitality

AI Application

Faible confiance

FLYR Hospitality, formerly Pace Revenue, is a cloud revenue management and business intelligence platform for hotels, hostels, serviced apartments and hotel groups. Optimize is an AI pricing engine that recalculates room prices and stay restrictions (such as minimum length of stay) every hour for each room type, night and length of stay, using PMS bookings, rate shopper data and local event signals, and can push them to the PMS automatically or after review. Duetto describes the engine as modelling a range of demand scenarios and improving through reinforcement learning. Insights is a BI layer with natural-language querying plus a Workbook Agent and Dashboard Agent that build charts and summaries from plain-English requests; Planning provides AI baseline forecasts and budgeting, and Groups evaluates group displacement and blended rates. Users are commercial and revenue teams; the vendor reports 2,300+ properties, with customers such as Ennismore, Lark Hotels and Premier Inn Middle East. The business was acquired by Duetto on 8 October 2026, while the legal entity in the privacy policy is still Prix Limited (UK). Rated medium: hotel room pricing is not an Annex III high-risk use under the EU AI Act and the system does not decide on individuals, but it processes guest-level reservation data from the PMS, prices most nights autonomously (vendor figure 88%) and pushes prices to distribution channels, and its natural-language Insights features likely rely on a large language model whose provider is not disclosed. Public assurance is thin: there is no trust page, SOC 2 or ISO 27001 statement for the hospitality platform, no customer sub-processor list and no public DPA, and the October 2026 change of ownership means entity, hosting and contracts may change. The rating would fall to low if Duetto confirms SOC 2 Type 2 coverage, EU or UK hosting, a sub-processor list and no third-party LLM training on customer data; it would rise if guest personal data is sent to an external LLM without a DPA, or if pricing were personalised to individual guests.