We haven't deployed in hospitality yet. But the forward deployed model we run across e-commerce and legal applies directly to hotels, restaurants, and leisure businesses. Here's what we'd do.
Hospitality runs on thin margins with high fixed costs. Labour typically represents 30–40% of revenue. Property costs are inflexible. Revenue fluctuates dramatically with seasons, events, and economic conditions. These are exactly the conditions where our forward deployed model creates the most impact.
In e-commerce, we've deployed AI pricing that delivered +77% revenue in five weeks. We've automated operations workflows that cut headcount costs. The patterns transfer: dynamic pricing is dynamic pricing whether it's a SKU or a room night. Headcount optimization is the same discipline whether you're scheduling warehouse pickers or front-desk staff.
We haven't done this work in hospitality specifically — so we won't pretend we have. But the operational problems are familiar: fragmented tech stacks (PMS, CRM, booking engine, channel manager, POS), manual revenue management, OTA dependency at 15–25% commission, and back-office processes that haven't been rethought in a decade. We know how to fix these problems. We just haven't done it in a hotel yet.
These are the services we run in other industries that would transfer to hospitality. Each one follows our forward deployed model: we don't advise, we deploy and run the work.
The same AI agents we deploy in e-commerce and legal — adapted for hospitality. Revenue management, guest experience, operations, and analytics agents that would replace manual processes like rate updates, guest communication, and demand forecasting.
Deep dive →We'd apply the same elasticity modelling that delivered +77% revenue in e-commerce to room rates, event pricing, and F&B. Automated rate recommendations with margin floors, competitive constraints, and real-time demand monitoring — the methodology is proven, just not yet in hospitality.
Deep dive →We build CDPs that unify customer data across fragmented systems — we'd do the same across PMS, CRM, booking engine, and POS. RFM scoring, lifetime value, churn prediction. The goal: reduce OTA dependency and enable personalized upselling based on guest segment.
Deep dive →Our principle: automate the work before removing the role. In hospitality, this would mean AI-powered scheduling matched to demand patterns, automation of booking management and guest communications, and phased headcount reduction across admin functions.
Deep dive →Infrastructure audits are infrastructure audits — industry doesn't matter much. We've cut £276K/year from one client's hosting bill. The same methodology applies to hospitality tech stacks: PMS hosting, SaaS sprawl, and cloud services where you're overpaying.
Deep dive →We'd shift bookings from high-commission OTAs to direct channels using the same lifecycle marketing automation we run elsewhere. Acquisition cost optimization, attribution that connects spend to revenue per guest, and pre-stay/in-stay/post-stay automation.
Deep dive →We've taken CSAT from 59% to 80% in other industries using AI-powered support. For hospitality, we'd deploy AI concierge for guest queries, booking modifications, and service requests — reducing front-desk workload while improving satisfaction scores.
Deep dive →Dynamic recommendations based on guest history and preferences. AI-powered search across your booking engine. We'd apply the same test-and-measure approach we use in e-commerce personalisation — every change tested against conversion and revenue impact.
Deep dive →These numbers are from e-commerce and legal engagements — not hospitality. We include them so you can see the forward deployed model works. The methodologies transfer; the specific results will vary.
AI dynamic pricing for hospitality uses the same elasticity modelling principles we deployed in e-commerce — but applied to room rates, event pricing, and F&B. The engine analyzes historical booking data, seasonal patterns, competitor rates, and demand signals to recommend optimal prices for every room type, every day. Instead of a revenue manager manually adjusting rates based on gut feel, the AI model processes thousands of data points and generates recommendations with margin floors enforced automatically.
Yes. We follow the same automation-first principle used across all our engagements: automate the work before removing the role. In hospitality, this means AI-powered booking management, automated guest communications, chatbot-first support for routine queries, and intelligent scheduling that matches staffing to actual demand patterns rather than fixed rotas. The result is fewer hours wasted during quiet periods while maintaining full service during peak times.
Not yet. Our current engagements are in e-commerce and legal. But the forward deployed model — dynamic pricing, headcount optimization, customer data platforms, cloud cost reduction — applies to any business with variable pricing, seasonal demand, and high labour costs. Hotels, venue operators, and restaurant chains are a natural fit.
We start delivering results within two weeks. Quick wins like report automation, vendor consolidation, and KPI dashboards come first. Dynamic pricing typically shows measurable revenue uplift within 3-5 weeks of deployment. We stay and operate, continuously finding new margin improvements with monthly board reporting against EBITDA targets.
Most hospitality businesses have guest data scattered across PMS, CRM, booking engines, and POS systems with no unified view. We build customer data platforms that consolidate this into a single guest profile with RFM scoring, lifetime value calculation, and churn prediction. This enables targeted marketing (reducing wasted promotional spend), dynamic pricing based on guest segment, and personalized upselling that increases revenue per booking without additional acquisition cost.
We're honest about where we've deployed and where we haven't. Check our proof points from live engagements — then book a call if you want to explore what we'd do for your hospitality business.