AI-powered data for cruise and tour revenue teams

Cruise pricing intelligence delivered to your warehouse.

Every competitor sailing and fare, by cabin, storefront, and snapshot date, with promos and sold-outs flagged. You pick the data schema, competitor, and cadence; we build and maintain everything.

1
Monitor
2
Match
3
Deliver
robots.comp_set
Monitored on your cadence
Illustrative data

Your named competitors

A
Competitor A
River · 5 storefronts, local currency
Robot live
B
Competitor B
Ocean · cabin × date × occupancy
Robot live
C
Competitor C
Escorted tours · promos and inclusions
Robot live
D
Competitor D
River · sold-out signal per cabin
Robot live
Fully managed
bot-protected flows
your cadence

Apples to apples

Your brand · River Queen
Balcony Suite
air
drinks
Tier 2matched cabin
Competitor A · Aria Class
Deluxe Balcony
drinks only
Tier 2matched cabin
Competitor B · Vista Class
Panorama Stateroom
all inclusive
Tier 2matched cabin
Same sailing window, same tier. Inclusions stay on the row so effective price is comparable, not just the headline fare.

Warehouse-ready feed

ship
departure
cabin
fare pp dbl
inclusions
River Queen
2027-07-04
Balcony Suite
EUR 4,995
air, drinks
Aria Class
2027-07-08
Deluxe Balcony
EUR 4,650
drinks
Vista Class
2027-06-30
Panorama Stateroom
EUR 5,240
all inclusive
River Queen
2027-08-12
French Balcony
EUR 3,890
air
Aria Class
2027-08-19
Deluxe Balcony
EUR 4,410
drinks
One row = sailing × cabin category × market × snapshot date
Lands in
your warehouse, lake, API, or files
Snowflake
Microsoft Fabric
Databricks
your S3 or Blob bucket
your REST API and webhooks
or SFTP, CSV, Sheets
100% of your comp set
Access data from any competitor with 100% coverage.
Days to first data
A real sample first, live feeds ready to deploy in weeks.
SLA-backed
Row-level accuracy and data quality commitments.
Your schema, your cadence
Built to order for your data structure and needs.

You're pricing next season on last quarter's data.

What pricing teams at cruise lines and tour operators tell us, over and over.

We only get updated competitor rates 3 or 4 times a year. The market moves monthly.

Someone on the team is spending a full week each quarter rebuilding spreadsheets.

The data never lands in our warehouse in a shape we can query.

We pay enterprise prices for a fraction of the volume we actually use.

The same sailing is a different price in every storefront, and we only see one.

Why Browse AI's cruise pricing intelligence is different

Designed for sailings, not retrofitted from hotels.

Every mainstream pricing tool was built for hotels, leaving cruise and tour teams choosing between an enterprise incumbent, a do-it-yourself scraping project, and an analyst with a spreadsheet. We start from the sailing: multi-step booking flows, cabin grades, storefront pricing, promo bundles.

1

Tell us your comp set

The competitors, markets, data schema, and delivery cadence.

2

Get a real sample in days

We'll deliver a sample dataset built on the competitors you name, for your analysts and data architect to review.

3

Feeds go live in weeks

We build and maintain every robot to capture, transform, and deliver your data accurately. A broken scraper is our problem to fix, not your silent data gap.

4

Scale when you're ready

Add more competitors, more markets, more storefronts, or hotel rates for annual contracting.

One year terms
Contracts sized to your actual volume
Data quality commitments in writing

Create a unified dataset from multiple sites and sources.

Sailings and price points from every site in your competitor set, joined by sailing key: ships matched, cabin grades mapped, inclusions normalized. Add sources whenever you like; your team always queries one comparison-ready dataset.

Competitor booking sites

brand.com storefronts
every market & currency

OTAs & resellers, where they matter

Booking.com
Expedia
cruise OTAs

Sailings

Keys
sailing key
departure date
Itinerary
brand
ship
itinerary name
region or river
embark port
disembark port
nights
Market
market (storefront)

Price points

Keys
sailing key
snapshot date
Price
cabin category
occupancy basis (pp dbl)
fare
currency
market
fare type
Flags
inclusions
meal plan
availability
promo text
air
drinks
excursions
gratuities
One row = sailing × cabin category × market × snapshot date. Every snapshot builds your booking curve history, and you own it.
sailing key
ship + cabin matching

Rhine, Amsterdam to Basel, 7 to 8 nights, summer 2027 departures: River Queen Jul 4, Aria Class Jul 8, Vista Class Jun 30. Mapped cabin tier: Balcony (Tier 2). EUR storefront (DE), snapshot Aug 1, 2026. Illustrative data.

Brand
Their cabin name
Mapped tier
Fare pp dbl
Effective per night
Availability
Your brand
Balcony Suite
Tier 2
EUR 4,995
EUR 585
Available
Competitor A
Deluxe Balcony
Tier 2
EUR 4,650 lowest headline
EUR 605 not the cheapest
2 categories sold out
Competitor B
Panorama Stateroom
Tier 2
EUR 5,240
EUR 640
Available

Effective per night = headline fare normalized for nights and inclusions. Competitor A has the lowest headline fare and not the lowest effective price, because their fare excludes air.

US$5,410
UK£4,120
EU€4,650
AU$8,150

Same ship, same cabin, same date. Four storefronts, four prices. We keep every market native, in local currency, and never convert away.

Want to see this schema against your own comp set? Name 2 or 3 competitors and we will build the extract.

See it with your own comp set →

Fuel your revenue and pricing team with the live data they need.

Price positioning

Reprice a sailing against the competition at the same lead time, not against last quarter's spreadsheet.

Promo response

See a competitor's flash sale the week it happens and choose your timing and depth deliberately.

Pacing

Read where the market sits on the booking curve and know if you are ahead or behind at this point in the season.

Deployment planning

Watch which rivers, regions, and itineraries competitors are adding before the brochures come out.

Contracting

Extend the same feeds to hotel rates and walk into annual negotiations with the market in hand.

One gap can undo a year of pricing intelligence.

Miss a snapshot and it is gone for good. Data continuity and quality is at the core of our solutions: agents are monitored, breaks fixed, every row checked.

Booking curve chart: competitor Balcony-tier fare by months to departure, with two step-downs and a shaded missed-snapshot window. A silently broken scraper misses this window and the history cannot be rebuilt.

Your renewal inside 6 months? That is exactly the right time to run a sample comparison.

Book a call before your renewal →
For the data architect

Warehouse-ready, joined, and ready for dbt

Sailings and pricing land as structured tables formatted for your lake, joined by sailing key, ready for dbt. Your data architect can test the REST API during evaluation, before anyone signs anything.

Prefer files or sheets? We deliver to whatever your stack actually is.

Browse AI Agents
Extract your comp set and monitor for changes based on your cadence.
QA and normalization
Ship mapping, cabin tiers, inclusions flags, row-level checks
Your warehouse
Microsoft Fabric · Snowflake · Databricks
Your tools
Power BI · Tableau · revenue management systems
Webhook
REST API
S3 or Azure Blob
SFTP and CSV
Google Sheets

Why do cruise revenue and pricing teams choose Browse AI?

Enterprise vendors
DIY & LLM scripts
Fixed-panel datasets
Manual spreadsheets
Browse AI
Whose comp set
Their panel
Yours, if it survives
Their panel
Yours
Yours, changed same-day
Cadence
Their schedule
Whenever it runs
Fixed
3 or 4 times a year
Yours: weekly or monthly
Ship & cabin matching
Partial
Build it yourself
None
By hand, in Excel
Built in
Data quality
Their QA, opaque
Whatever the script returns
As sold
Typo-prone
Row-level checks, in writing
Delivery
Dashboard
Raw files
File export
Spreadsheets
Your warehouse, dbt-ready
Service
Ticket queue
You are the support
Thin
Your own time
A named team, and breaks are our problem
Contract
Annual enterprise
An engineer's salary
Subscription
Analyst time
Right-sized, no auto-renewal

See how your comp set looks in this table, with your competitors and your markets.

See your own comparison →

Book a call

Book an exploratory call

Thirty minutes with our team on your needs and how we can help. You leave with:

✓ Your sample extract scoped live on the call: the competitors, storefronts, and cabin tiers you actually price against, delivered to your inbox after

✓ An honest read on your comp set: which sites are straightforward, which are hard, and why

✓ A recommendation for the right setup: self serve, a one-time build, or fully managed

Galen, Browse AI
You will meet Galen
He runs these calls and works with cruise and tour revenue teams globally.

Questions we get

Do you have historical cruise pricing data?

No. If you need ten years of backdata on day one, an incumbent panel has it and we do not. Your history starts accruing from your first snapshot, in your own warehouse, and it stays yours, including if you leave.

Can you handle bot-protected, multi-step booking sites?

Yes, that is the core of what we do. Cruise quote flows (cabin by date by occupancy by market) are exactly the extractions our managed services team runs in production today.

What happens if a site genuinely cannot be captured?

We tell you, before it becomes a quiet gap. Occasionally a site's architecture makes reliable extraction impossible; when that happens we say so directly and work the alternatives with you.

Who owns the data?

You do. Feeds land in your warehouse and the accumulated history is yours, not a dashboard subscription that disappears when the contract ends.

How is this different from RateGain?

RateGain tracks cruise pricing on six itinerary factors and shows it in a dashboard. We deliver the full price point, inclusions and market versioning included, into your warehouse.

Can we change the comp set or the schema later?

Yes, that is the point. Competitors, markets, cabin categories, cadence, and fields all flex; most changes are same-day.