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Solution

Every fare and rate move, captured by route and date

Real-time competitor fares, hotel rates, room availability, and meta-search positioning across every route, date, and property that decides your revenue.

<15 mindetection latency on flagged routes
10M+fare and rate observations daily
99.9%pipeline uptime
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Travel pricing changes by the minute, by route, by date.

Airfares shift on inventory, demand, and competitor moves multiple times an hour. Hotel rates flex by room type, length of stay, member tier, and channel. The team that sees the move first sets the price, the team that sees it Monday gets the leftover bookings.

Most revenue teams see the easy data, not the actual market.

GDS feeds give you base fare, not the actual price a customer sees on Booking.com after the meta-search and member rate apply. Hotel rate-shopper tools cover web, miss app-only deals, and lag on dynamic packages. The full competitive picture is fragmented across systems.

We capture every fare, every rate, every route-date combination, on the surfaces customers actually see.

Web, app, meta-search, member rate, package-bundled. Cycles as low as every 15 minutes on flagged routes and properties. Delivered structured, deduplicated, in your schema.

Fare and rate moves, by the minute

Detect competitor fare and rate changes within minutes of them happening. Revenue management responds inside the same booking window, not the next quarterly cycle.

Web, app, meta, and member rates

The same room or fare often shows three prices: public web rate, app-only deal, member rate. We capture each as a separate record, all attributed to the same listing.

Every route and property in scope

Domestic, international, premium, low-cost. Member rates, OTA rates, direct rates. Captured across every route-date and property in your competitive set.

Key insight

Travel pricing is won in 24-hour windows. The airline that knows competitors' fare moves at 9 AM can price its 11 AM departures correctly. The hotel that sees a competitor drop rate on Friday for the weekend wave can hold or undercut before the customer commits. Speed of detection is revenue.

How it works

The extraction pipeline

From target spec to your warehouse, every fare and rate record passes through these stages. You see the output. We run everything in between.

01

Target spec

Routes, dates, properties, channels, cadence, and schema locked from your pilot scope.

02

Source orchestration

Web, app, meta-search, and direct-site extraction across every channel in scope, in parallel.

03

Capture

Geo-correct sessions per market, app-layer where needed, member-tier capture where authorized.

04

Validation

Schema, range, deduplication, fare-class normalization, currency normalization.

05

Delivery

CSV, JSON, REST, or direct push to your warehouse, in your spec.

Coverage

Platforms we monitor

Travel rate intelligence runs against every surface where a traveler compares options. OTAs, meta-search, direct sites, and apps. Each surface has its own pricing logic and its own defenses.

NS

OTAs

Booking.com, Expedia, Agoda, MakeMyTrip, Goibibo, Trip.com, Hotels.com

1

Meta-search

Google Flights, Kayak, Skyscanner, Trivago, Google Hotels positioning

Airline direct

Carrier websites and apps with member rates and fare classes

Hotel chains direct

Marriott, Hilton, IHG, Hyatt direct rates and member tiers

NS

Vacation packages

Bundled flight-plus-hotel deals across OTAs and aggregators

10M+Fare and rate observations captured daily across routes and properties

Data landscape

The data we extract

Every fare and rate observation from every monitored platform, normalized into one travel schema, delivered on your cadence.

₹

Fare or rate

Base fare, taxes, fees, total price, member rate, voucher-applied rate, currency

Inventory

Cabin or room type, available seats or rooms, fare class, refundability flag

Trip parameters

Origin, destination, departure date, return date, length of stay, number of pax

Source attribution

Platform, channel (OTA, meta, direct, app), member tier, capture timestamp

1

Meta-search position

Rank in meta listing, sponsored flag, badge, displayed total

Promotions

Voucher, member discount, package bundle, free-cancellation flag

This is a representative sample of the data we extract. We customize every extraction to your exact requirements. If you need a data point not listed here, we will add it to your pipeline.

Sample output

What a single record looks like

This is a representative payload from a real fare and rate extraction job. Field names, schema, and delivery format are scoped to your spec at pilot time.

{
  "extracted_at": "2026-05-07T12:18:09Z",
  "platform": "booking.com",
  "channel": "web",
  "market": "US",
  "trip_type": "hotel",
  "search_params": {
    "destination": "Singapore",
    "check_in": "2026-08-12",
    "check_out": "2026-08-15",
    "guests": 2,
    "rooms": 1
  },
  "property": {
    "id": "BKG-145987",
    "name": "Marina Bay Sands",
    "chain": "Marina Bay Sands",
    "stars": 5,
    "matched_property_id": "OUR-PROP-MBS"
  },
  "rate": {
    "room_type": "Deluxe King Garden View",
    "base_rate": 489.00,
    "taxes": 73.35,
    "fees": 28.00,
    "total_per_night": 590.35,
    "currency": "USD",
    "member_rate": 469.00,
    "is_refundable": true
  },
  "meta_position": {
    "rank_in_results": 3,
    "is_sponsored": false,
    "badge": "Genius Discount"
  },
  "promotions": [
    {
      "type": "member_discount",
      "value_percent": 10
    }
  ]
}

Schema

Field-level reference

Every record conforms to a stable schema. Your engineering team can integrate against this spec before the pilot starts.

extracted_atISO 8601

UTC capture timestamp

2026-05-07T12:18:09Z
platformstring

Source platform name

booking.com
channelenum

web, app, meta, direct

web
trip_typeenum

flight, hotel, package, car

hotel
search_params.destinationstring

Destination or arrival city/airport

Singapore
search_params.check_inISO date

Check-in or departure date

2026-08-12
search_params.check_outISO date

Check-out or return date

2026-08-15
search_params.guestsnumber

Number of pax

2
property.idstring

Source-platform property/flight identifier

BKG-145987
property.namestring

Property or carrier name

Marina Bay Sands
rate.base_ratenumber

Pre-tax fare or rate

489.00
rate.total_per_nightnumber

Total displayed price including taxes and fees

590.35
rate.currencyISO-4217

Currency code

USD
rate.member_ratenumber / null

Loyalty or member rate where exposed

469.00
rate.is_refundableboolean

Refundable cancellation policy

true
meta_position.rank_in_resultsnumber

Listing rank in search results

3
meta_position.is_sponsoredboolean

Paid placement

false
meta_position.badgestring / null

Platform badge or label

Genius Discount
promotions[]array<object>

Active promotions per record

[{type, value_percent}]

Delivery formats

How you receive the data

You define the format. We handle the rest.

CSV

Daily or hourly drops

Scheduled flat-file delivery. Clean, deduplicated rows with the columns you define.

{}
{}

JSON

Nested or flat schema

Structured JSON files for direct ingestion into your data pipeline or analytics tools.

API

Real-time access

REST API with real-time access to the latest extracted data. Webhook support included.

Direct warehouse

Zero-touch delivery

We push directly to your Snowflake, BigQuery, Redshift, or S3 bucket. Zero manual steps.

Custom setup

Talk to us

Need a different format, frequency, or integration? We build it for you at no extra cost.

Use cases

How teams put fare and rate data to work

From pricing teams to category managers to operations leads, here are the most common ways fare and rate data drives decisions.

₹

Revenue management, dynamic repricing

Feed competitor fare and rate moves into your revenue management system. Reprice routes, properties, and length-of-stay tiers on real signals, inside the same booking window.

Distribution strategy, channel mix

See how your rates are positioned on each OTA, meta-search, and direct channel. Identify where channel commission is eroding margin and where direct booking can be defended.

Network and route planning

For airlines: track competitor fare patterns route by route, identify where new entrants are pressuring yield, and evaluate route economics on real demand-and-pricing signals.

Promo and loyalty response

Catch competitor flash sales, voucher pushes, and loyalty-tier promotions the moment they activate. Decide whether to match, undercut, or hold inside the same booking window.

Hotel rate parity and channel compliance

For hotel chains: detect rate-parity violations across OTAs, channel managers, and direct sites. Hold parity, or document violations for renegotiation.

Leadership, market scorecard

Weekly board-ready competitive market reports across routes, properties, and channels. Share of voice, parity scorecard, win-loss against competitive set.

Tech specs

What we run at scale

Every fare and rate engagement runs against these baseline specs. Your scope can move freshness, throughput, or geo coverage to whatever you need.

<15 min

Detection latency on flagged routes

10M+

Fare and rate observations daily

99.9%

Pipeline uptime

100+

Markets and currencies covered

15 min

Minimum extraction cycle

99%+

Records passing validation

Challenges

Why fare and rate data extraction is hard

If extraction were easy, you would do it yourself. Here is why it is not.

01

Fare and rate change with every search

Airline pricing engines respond to demand on every query. Hotel rates flex on length of stay, lead time, and member tier. Capturing reliably means capturing on a fixed schedule, with consistent search parameters, across thousands of route-date combinations.

02

OTAs and meta-search fight extraction harder than retail

Booking.com, Expedia, and meta-search aggregators run aggressive bot detection. Search results are personalized by IP, geography, browser, prior session. Capturing the rate a clean customer sees requires geo-correct, de-personalized session capture.

03

Member rates and loyalty tiers are a separate problem

Public rate, member rate, elite-tier rate, app-only rate. Each is a different surface. Most providers capture only public rate and miss the prices that decide loyal-customer bookings.

04

Inventory states make pricing meaningful or meaningless

A fare at 10 AM with 3 seats left is a different signal than the same fare with 47 seats left. Capturing seat count, room availability, and fare class alongside price is what makes the data actionable.

05

Cross-currency and timezone normalization at scale

Routes cross currencies and timezones constantly. Normalizing fare and rate data across markets, with correct currency and time references, is a data-engineering problem most teams underestimate.

06

Building it in-house costs more than the data

Engineers, geo-routing, login automation, OTA-specific scrapers, monitoring, on-call. Internal projects spend 6 to 12 months and end up with a fragile system that misses the rate moves that mattered.

Why us

Why Clymin for fare and rate

We are not a tool. We are the team you call when the data matters too much to get wrong.

We solve what others can't

Travel rate intelligence at scale across OTAs, meta-search, direct sites, and apps. App-layer where the deal lives, geo-correct sessions for clean rate capture, every cycle as low as 15 minutes.

We prove it before you pay

Free pilot on your routes, your properties, your competitive set. Sample data within 1 to 3 days. You evaluate against your existing rate-shopper or revenue management feeds before any commitment.

You pay only for data delivered

Per record, no setup fees, no per-route charges, no per-property charges. One metric: cost per record. If we don't deliver, you don't pay.

Your identity stays protected

We do not display client logos or name-drop. Travel pricing intelligence is sensitive. Your competitors should never know you are watching.

100B+

Data points extracted

24/7

Pipeline uptime

Real-time

Data delivery

100K+

Points of interest covered

Proven at enterprise scale. We operate continuous competitive intelligence infrastructure for one of the world's largest quick commerce platforms.

Industries served

Who buys fare and rate data

The verticals where fare and rate extraction creates the most leverage.

Travel OTAsAirlinesHotels & AggregatorsManagement ConsultingFinancial Services

See every competitor fare move inside the booking window

Tell us your routes, your properties, your competitive set. Pilot data in 1 to 3 days. No commitment.

Start a Free PilotBook a Meeting

FAQ

Travel Fare & Rate Intelligence data extraction FAQ

OTAs (Booking.com, Expedia, Agoda, MakeMyTrip, Goibibo, Trip.com, Hotels.com), meta-search (Google Flights, Kayak, Skyscanner, Trivago, Google Hotels), airline direct sites and apps, hotel chain direct (Marriott, Hilton, IHG, Hyatt), and any travel platform we are scoped to add during pilot.

Cycles as low as every 15 minutes on flagged route-date combinations and high-priority properties. Standard cadence is hourly to daily based on scope.

Yes, where authorized. We capture public rates by default. Member-tier and loyalty-tier rates require account access provided by the customer for capture.

Yes. Rank in meta listing, sponsored vs organic flag, badges, and total displayed price are captured per record. You see how you rank as well as what you charge.

Yes. 100+ markets, every major currency. Currency, timezone, and fare class are normalized at validation, before delivery.

Yes. Available seats, room availability, fare class, and refundability flag are captured per record. A fare with 3 seats left is a different record from the same fare with 47 seats left.

CSV, JSON, REST API, or direct push to your data warehouse: BigQuery, Snowflake, Redshift, S3. You define the schema.

We extract publicly available data. Member-rate capture requires explicit account access from the customer. Use of extracted data is the customer's responsibility under their jurisdiction.