Completed
Hospitality & Events
AI-Native RSVP & Operation Management
Event operations for weddings and summits — RSVPs, travel, rooms and guest messages handled in one live command center.
< 4 hrs
RSVP follow-up, down from 5–7 days
Client project · Hospitality
AI-Native Reputation Command Center
Guest reviews turned into department tasks — every complaint traced to its cause, routed with a deadline, and its cost in lost revenue shown.
- Client
- Luxury hospitality group
- Where
- Northeast India
- Platform
- Web app
- Built by
- Fortiv Solutions
- Status
- Completed
01 / 05
Overview
We built this reputation platform for a luxury hospitality group running tea estate bungalows, wildlife lodges and hill resorts. It reads reviews from Google, OTAs and in-stay WhatsApp and QR feedback in several languages, groups complaints by their real cause, routes each issue to the right department with a deadline, catches unhappy guests before they check out, and shows how much revenue each unresolved issue puts at risk.
- The client
- Tea estate bungalows, wildlife safari lodges and hill resorts across several states, many in remote locations.
- The brief
- Reviews were read once a month, the same complaints kept coming back, and unhappy guests usually posted a public review before anyone at the property knew there was a problem.
- Focus
- Guest reviewsRoot-cause analysisRevenue at risk
02 / 05
Specification
Who it was built for, what it runs on, what it connects to and the rules it follows.
- Client
- Luxury hospitality group
- Location
- Northeast India
- Platform
- Web app
- Delivered by
- Fortiv Solutions
- Project scope
- Tea estate bungalows, wildlife safari lodges and hill resorts across several states, many in remote locations.
- Built with
- AI Insights, Multilingual AI, Predictive Analytics, Workflow Automation
- Connects to
- OpenAI
- PostgreSQL
- REST APIs
- Rules built in
DPDP Act
Guest data protection
Phone numbers, room numbers and card details are removed from review text before analysis.
SLAs
Deadlines by severity
Safety issues are acknowledged in 15 minutes; comfort and service issues within 30 minutes to an hour.
Evidence
Proof of fix
Departments upload a photo of the fix before an issue can be closed.
Offline
Works at remote lodges
Feedback is stored on site during connectivity outages and synced when the link returns.
03 / 05
Proposed features
What the system was designed to do for the client's teams.
01
Every review in one place
Google, TripAdvisor, Booking.com, MakeMyTrip, Agoda and in-stay WhatsApp and QR feedback together.
02
Complaints grouped by cause
Reviews about cold rooms, slow hot water or late breakfasts grouped into the one issue behind them.
03
Tasks for each department
Each issue goes to housekeeping, F&B, engineering, front office or guides with a deadline.
04
Catch unhappy guests mid-stay
A WhatsApp check-in during the stay alerts the manager before a guest posts a bad review.
05
Revenue at risk
Shows how much booking revenue each unresolved issue is likely to cost, in rupees.
06
Replies in the brand's voice
Drafts thoughtful review replies in several languages for managers to approve.
04 / 05
Proposed solution architecture
What Fortiv built, what it connects to, and what passes between them.
Sensing & ingestion
- Public OTAs & Google
- In-stay WhatsApp & QR
- Property management systems
- Weather data
Ingestion bus
- Event streams
- Offline edge bufferRemote lodges
AI processing core
- Multilingual text parsing
- Aspect sentiment engine25 hospitality aspects
- Root-cause clustering
- Trend forecaster
Action center
- Department SLA dispatcher
- Revenue-at-risk simulator
- AlertsWhatsApp / SMS / push
Data stores
- PostgreSQLTickets, audit logs
- Vector databaseReview embeddings
- RedisLive scores
05 / 05
Business outcome
Recurring complaints have fallen sharply, most in-stay issues are resolved before the guest leaves, and review analysis takes minutes a week instead of hours.
Figures as reported by the client.
−65%
Recurring guest complaints
> 72%
Of in-stay issues resolved before checkout
< 45 min
Weekly review analysis, down from 12 hours
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