Car Rental Data
Agent-ready
car rental
data.
On-demand rates, ancillary data, event data, location intelligence, and filed-rate views — across channels, locations, and booking journeys. Clean, semantically structured car rental data built for pricing teams, internal systems, market research, and modern AI-driven workflows.
{
"dataset": "car_rates_ondemand",
"entity": "rental_offer",
"dimensions": ["pickup", "dropoff",
"supplier", "channel"],
"components": ["base_rate", "taxes",
"insurance", "fuel_policy"],
"delivery": ["api", "shop_manager", "vizly"]
}
"dataset": "car_rates_ondemand",
"entity": "rental_offer",
"dimensions": ["pickup", "dropoff",
"supplier", "channel"],
"components": ["base_rate", "taxes",
"insurance", "fuel_policy"],
"delivery": ["api", "shop_manager", "vizly"]
}
Data Layer
Agent-ready: a new standard in data.
Every dataset is normalised, semantically defined, and delivered on demand — so it can be used directly by revenue teams, BI systems, and agent-driven products.
- On-demand instead of daily dumps Fresh competitive pricing whenever your system pings the API.
- True retail price visibility Coverage across OTAs, metasearch, and brand.com — not just filed or GDS-style views.
- Full trip-cost structure Rates, taxes, insurance, fuel policy, mileage, and ancillaries mapped apples-to-apples.
- Built for systems and teams Ready for RMS, BI, operator workflows, mobility platforms, and AI agents.
Three Cohorts
Built for teams that need on-demand data,
not daily dumps.
Three distinct audiences rely on the same agent-ready data foundation: agent builders, internal systems, and data teams.
Agent Builders
Use semantically structured, on-demand car rental data to build AI agents, mobility products, and automated workflows without relying on brittle legacy feeds.
Learn More →Internal Systems
Feed RMS, BI, pricing, and operational systems with high-frequency data updates that stay aligned with how internal systems actually run.
Learn More →Data Teams
Give analysts and market research teams clean, structured datasets they can query, model, benchmark, and interrogate at scale.
Learn More →Car Rental Data
Core datasets powering car rental decisions.
Access each dataset independently, or combine them into a unified intelligence layer for pricing, BI, and agent-driven systems.
Datasets
On-Demand API
Live customer-facing rates, exactly when you need them.
Built for teams that require on-demand data updates, rather than daily data dumps. Query real retail prices on demand across suppliers, locations, dates, and channels — semantically defined for immediate machine understanding.
{ "pickup_location": "LHR", "dropoff_location": "LHR", "pickup_datetime": "2026-07-12T10:00:00Z", "supplier": "Avis", "channel": "metasearch", "vehicle_class": "Economy", "total_trip_cost": 248.70, "currency": "GBP", "fuel_policy": "full_to_full", "mileage_limit": "unlimited" }
| Field | Description |
|---|---|
| pickup_location | Airport, city, or station collection point |
| supplier | Rental brand or merchant captured |
| channel | OTA, metasearch, or brand.com source |
| vehicle_class | Standardised car category |
| total_trip_cost | Customer-facing total price |
| fuel_policy | Normalised policy for apples-to-apples comparison |
- Revenue systems that need more than a morning data dump
- Business intelligence workflows that refresh throughout the day
- AI agents that need current pricing to avoid hallucinating
- Deep competitive monitoring across hundreds of channels
Ancillary Data
The ancillary layer traditional suppliers miss.
Capture insurance offers, child seats, extra drivers, fuel products, mileage options, and other ancillaries directly from the booking journey — standardised so total rental propositions can be compared apples-to-apples.
Insurance bundleCaptured and normalised
Extra driverVariable by market
Child seatsStructured by type
Fuel productMargin pressure risk
| Field | Description |
|---|---|
| ancillary_type | Insurance, equipment, policy, or service add-on |
| price | Customer-facing ancillary price |
| bundle_logic | Included, optional, or package-based presentation |
| coverage_terms | Normalised insurance or waiver conditions |
| availability | Whether the ancillary is offered for the searched journey |
| display_stage | Where in the booking flow the ancillary appears |
- Ancillary benchmarking and proposition design
- Total-cost comparison beyond headline rate
- Manual crawl replacement for commercial teams
- Margin analysis across products and markets
Demand Signals
Event data for demand-aware pricing and planning.
Track concerts, conferences, sports fixtures, public holidays, and local demand-shaping events that influence airport and city rental pricing. Structured so pricing teams and models can align market movements to real-world causes.
Event type
Conference
Corporate demand
Market impact
High
Peak compression
| Field | Description |
|---|---|
| event_name | Name of event or demand driver |
| market | City, airport catchment, or region impacted |
| event_dates | Start and end dates with timing context |
| event_type | Conference, sports, concert, holiday, and more |
| impact_level | Estimated significance for local demand |
| source_context | Underlying event source and metadata |
- Demand-aware pricing strategies
- Location development and fleet planning
- Explaining market spikes to BI and RMS teams
- Feature engineering for forecasting models
Filed Rates
GDS and filed-rate views for baseline comparison.
Track filed rates and GDS-distributed pricing alongside your on-demand retail views. This provides a structured baseline for understanding how traditional filed inventory compares with the prices customers actually see in market.
Distribution viewFiled baseline
Filed distributionCustomer-facing context
| Field | Description |
|---|---|
| channel_type | GDS, corporate, broker, or other filed distribution source |
| supplier | Rental brand or parent company filing the rate |
| rate_code | Filed rate identifier or program reference |
| vehicle_class | Filed vehicle category for comparison |
| base_rate | Published filed rate before retail add-ons |
| capture_timestamp | When the filed rate was observed and normalised |
- Comparing filed and retail pricing views side by side
- Understanding legacy distribution alongside live market pricing
- Benchmarking rate code behaviour across suppliers and programs
- Creating a baseline for RMS, BI, and market research workflows
Enrichment Data
Location and demand context, structured.
Structured geospatial and location-level data that provides context around pricing, demand, and supply across rental locations. It also incorporates nearby hotel room rates to help teams understand broader travel demand and compression around each market.
{ "location_id": "LHR_T1", "type": "airport", "city": "London", "country": "UK", "demand_score": 0.82, "nearby_events": ["Concert", "Conference"], "competitor_density": 14, "avg_daily_rate": 68.40, "nearby_hotel_bar": 214.00 }
| Field | Description |
|---|---|
| location_id | Unique rental location identifier |
| type | Airport, city, suburb, or station classification |
| demand_score | Relative demand indicator for the location |
| competitor_density | Number of competing providers in-market |
| avg_daily_rate | Average market rate for the location |
| nearby_events | Event-driven demand context linked to the location |
| nearby_hotel_bar | Nearby hotel room rates used as a market compression signal |
- Location-level demand indicators for pricing and network planning
- Airport versus city segmentation and competitive context
- Competitor density mapping around key rental nodes
- Linking rates to demand, geography, event pressure, and nearby hotel pricing
Access & Control
From delivery to control to interrogation.
Aggregate Intelligence gives customers more than a feed. The API delivers agent-ready data, Shop Manager
gives teams control over their data shops, and Vizly provides a natural-language interrogation layer on top.
API
Our API is the delivery mechanism for agent-ready car rental data — designed for RMS, BI pipelines, applications, and AI agents that need structured, on-demand inputs.
GET /car-rates ?pickup=LHR &dropoff=LHR &pickup_date=2026-07-12
View API optionsShop Manager
Shop Manager gives teams hands-on control over how their data is configured, monitored, and managed. It is the operational layer for shaping and governing the data they receive.
ExploreBrowse markets
ConfigureSet journeys
ExportDownload data
Vizly
Vizly sits on top of the data as an interrogation layer, allowing customers to ask questions, explore outputs, and extract insight using natural language and domain-specific logic.
"Show total trip cost changes for economy rentals at Heathrow across OTAs versus brand.com next weekend."
Learn MoreDelivered by API. Controlled in Shop Manager. Interrogated through Vizly.
Modern car rental winners
run on better data.
Access the datasets you need, deliver them into your systems, control them in Shop Manager, or interrogate them instantly through Vizly.