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Hospitality margins leak through OTA commissions, manual reporting, and questions guests can't get answered. We plug those leaks with booking tech, data, and AI.
The reality on the ground
Hotels hand 18–25% of room revenue to OTAs because their own booking experience can't compete, then spend the month assembling occupancy and revenue reports by hand from each property's systems. Guests ask the same questions at every hour of the day, and the answers depend on who picks up the phone.
Groups that fix the direct-booking path, unify their property data, and put AI in front of routine guest queries routinely claw back both margin and management time.
What we deliver
Fast, mobile-first booking with live rates and channel-manager sync — winning revenue back from OTAs. Explore this practice →
PMS, POS, and channel data in one warehouse; occupancy, ADR, and RevPAR defined once, reported live. Explore this practice →
Multilingual WhatsApp assistants answering bookings, amenities, and requests around the clock. Explore this practice →
Soft weeks flagged 30 days out so pricing and promotions move before the discount panic. Explore this practice →
Daily flash reports, guest feedback loops, invoice flows, and OTA reconciliation on autopilot. Explore this practice →
Inquiry-to-itinerary pipelines with quote generation, supplier tracking, and payment schedules. Explore this practice →
Results we target
Targets based on engagements of this shape — actual goals are agreed per project, upfront, in writing.
Six hotels, one dashboard: RevPAR up 9% after data unification. Read the full sample case study for this industry.
Read case studyRepresentative scenarios
Honesty note: these are illustrative engagement scenarios — problem patterns we solve and the results a well-run engagement targets. They are not real client names or audited figures, and they'll be replaced by documented case studies as projects complete.
Client profile: Regional chain, 6 properties.
The problem: Six PMS exports, six POS formats, and Excel surgery meant month-end took a week and pricing questions went unanswered.
What we build: Central warehouse fed nightly by every property with one metric model, live chain dashboards, and automated 8 a.m. flash reports.
Typical results: Month-end from 7 days to 1, RevPAR up ~9% season-on-season, soft weeks flagged 30 days out.
Client profile: Boutique hotel group, 12 properties.
The problem: 70% of bookings came through OTAs at 18–22% commission; the direct site was slow and rateless.
What we build: Direct booking engine with live rates, channel-manager sync, a loyalty program, and one-click rebooking for returning guests.
Typical results: Direct share from 30% to ~52% in nine months, six-figure commission savings, repeat bookings up ~24%.
Client profile: Inbound tour operator.
The problem: Each inquiry meant hours assembling itineraries, hotel rates, and transfers into a quote — and slow quotes lost deals.
What we build: CRM with itinerary templates, supplier rate cards, and automated quote generation, plus WhatsApp follow-up sequences.
Typical results: Quotes in minutes instead of hours, follow-ups never missed, conversion up on speed alone.
Client profile: Restaurant group, 15 outlets.
The problem: POS data sat unused; nobody knew item-level profitability, wastage patterns, or which outlets underperformed and why.
What we build: POS data warehouse with menu-engineering dashboards — contribution per item, wastage, daypart, and outlet comparisons.
Typical results: Menu re-engineered on evidence, loss-making items fixed or cut, wastage down measurably.
Client profile: Resort property.
The problem: Front desk handled every query — directions, amenities, bookings, requests — and after-hours questions simply waited.
What we build: Multilingual AI concierge on WhatsApp grounded in the property's own information, with staff escalation for requests.
Typical results: Majority of routine queries answered instantly at any hour, front desk freed for in-person guests, reviews mention responsiveness.
Client profile: Online travel agency.
The problem: Supplier invoices, OTA payouts, and bank statements were matched by hand; discrepancies surfaced months late.
What we build: Automated reconciliation pipelines matching bookings to payouts to bank entries, with exception queues and aging reports.
Typical results: Reconciliation continuous instead of quarterly, leakage recovered, finance team off the matching treadmill.
Bring us the problem. We'll bring the plan, the build, and the numbers to prove it worked — agreed upfront, reported honestly.
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