We built an AI system that collects a global resort group's guest feedback from reviews, social media and surveys. It translates and prioritises each comment and sends tasks to staff. Service recovery became 85% faster.
Industry: HospitalityService: AI & AutomationUpdated
Overview
The challenge
Guest feedback was spread across TripAdvisor, Google Reviews, social media and internal surveys. Staff processed it by hand, with a 48-72 hour lag, so many service problems came to light only after the guest had checked out.
When reports did arrive, they were long summaries rather than clear jobs for a named team. Hotel managers could see that scores were slipping but not which room, shift or service needed attention first.
Serious guest complaints went unanswered while the guest was still on site, hurting the brand and the group's rankings on online travel agencies.
Without early signals from local feedback, the group could not adjust prices for its most valuable amenities.
Front-line staff received generic data reports instead of specific service tasks they could act on.
The solution
We built a live data flow that pulls in unstructured feedback from 12+ channels at once, including voice-to-text and social media APIs.
AI models translate 40+ languages and sort each comment into one of 150+ service categories, such as an HVAC failure or a housekeeping delay.
Each issue becomes a ticket sent straight to the property's PMS or the relevant service team, so someone can act on it right away.
Before and after
Before
48-72 Hour Lag
Manual weekly reports meant issues were fixed late and guests left frustrated.
After
Live prioritisation
Under 2 seconds from the moment feedback arrives to a clear instruction for staff.
Before
Keyword matching
Simple search terms missed nuance, and sarcasm in other languages slipped through.
After
Meaning and context
Deep-learning models read intent, tone and urgency across 40+ languages.
Before
Static reports
PDF reports were so general that operations teams ignored them.
After
Live tasks
Tickets go automatically to named staff members, with escalation if they are not picked up.
Key features
The system picks up small shifts in feedback patterns that point to guests who are unhappy but have not complained, and prompts staff to reach out early.
Business impact
92% classification accuracy
Each task is tracked from the original comment to its resolution, giving the group a clear record for guest experience reviews.
Business impact
100% traceability
Language AI runs on local servers at resort hubs, so responses stay under a second during busy check-out times.
Business impact
14% NPS increase
Results
85% Response Time
Issues that once waited up to 72 hours for a manual report are now handled 85% faster, often before the guest leaves.
Before: 72 HoursAfter: 85% Faster
14% NPS Increase
Fixing problems during the stay went hand in hand with a rise in guests saying they would book again.
Before: BaselineAfter: 14% Lift
92% Accuracy
The AI models identified urgent service issues more accurately than the old manual tagging.
Before: 80%After: 92%
Faster delivery
Reusing these components saved about 8 weeks of setup. The team spent that time on translation and the group's own service categories.
Masked guest names and personal details in feedback before any text reached the AI models or staff tickets.
Gave the team tested sentiment scoring for hotel feedback, which we tuned to the group's 150+ service categories.
Tracked open and resolved guest issues by property, so managers could see where tasks were piling up.
Passed shifts in local guest sentiment to the revenue team, so amenity prices could follow demand.
Trust and control
Feedback is anonymised and processed in-region, in line with each country's privacy laws.
PrivacyEvery task shows a confidence score and the original text, so a person can confirm the issue before acting.
Human reviewThe cloud setup keeps response times stable during seasonal travel peaks.
Scales with demandThe client owns 100% of the IP in the custom hospitality language models and task-routing rules.
100% ownedFeedback from TripAdvisor, Google Reviews, social media and internal surveys sat in separate systems and was processed by hand. Reports arrived 48 to 72 hours later, so many service issues surfaced only after the guest had checked out. Front-line staff got generic reports instead of clear tasks.
It collects feedback from 12+ channels, translates 40+ languages and sorts each comment into one of 150+ service categories, such as an HVAC failure. It then creates a ticket for the right team or the property's PMS, with escalation rules, in under 2 seconds from when the feedback arrives.
Guest issues are now handled 85% faster than under the old 72-hour cycle, often while the guest is still on site. Net Promoter Score rose 14%, and AI classification reached 92% accuracy, compared with 80% for manual tagging. Every task is logged from the original comment to its resolution.
If reviews and surveys reach your teams days too late, we can help you build a system that turns them into tasks while the guest is still with you.