Unified analytics dashboard pulling from siloed PMS and bookings.
Enterprise hospitality teams are frustrated by legacy PMS systems that fail to integrate with modern booking engines, creating data silos and requiring manual workarounds across multiple properties.
HospUnify aggregates data from legacy PMS and booking engines into a single dashboard without requiring API changes. It uses scheduled pulls and AI normalization for accurate multi-property reporting. Teams get occupancy insights and revenue forecasts instantly.
Enterprise hospitality teams managing multiple properties, such as hotel chains or resort groups
API-less aggregation with AI data normalization, focused on analytics rather than sync.
supportive
Pull from PMS exports or APIs.
Real-time occupancy, revenue metrics across properties.
Standardize disparate formats automatically.
Build and schedule PDF/CSV reports.
AI-powered revenue predictions.
Team permissions for views.
Automated email reports.
Compare properties anonymously.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| name | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| property_id | uuid | No |
| type | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| source_id | uuid | No |
| data | jsonb | No |
| pulled_at | timestamp | No |
Relationships:
/api/sourcesAdd data source
/api/dashboard-dataFetch unified metrics
/api/reportsGenerate report
Weekly pulls
Hourly pulls
Real-time
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 60 | 6% | $108 | $1,296 |
| Month 6 | 400 | 12% | $1,440 | $17,280 |
Unify PMS and bookings without integrations—get insights now.
Target Reddit r/hospitality and LinkedIn posts about reporting pains, offer free dashboards for their data. Partner with hospitality consultants for referrals.
Deep analytics
Requires full integration
No-API aggregation for legacy systems
Aggregated benchmarking data moat
Demand for analytics amid revenue recovery
Data normalization accuracy
User feedback loops
Chart library complexity
Use proven Recharts
Success: 85% normalization accuracy
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