Overview
Discovery API
Advisor
Developers
Case studies
Travel intelligence infrastructure

Turn a sentence into a shortlist worth booking.

MyEscapePlan understands traveller intent, ranks the destinations, dates and trip lengths worth exploring, then verifies only the options worth shopping against live provider inventory.
Across 40 internal benchmark requests (4 scenario families, Flights and Accommodation tracks), Discovery removes 67.7% of the logical shopping space while retaining 67.5% Recall@10.
Flights
Accommodation
Destinations
Experiences
TRAVEL DISCOVERY INFRASTRUCTURE

Discovery API

Turn open-ended travel intent into a small, ranked set of destinations, dates and trip options worth exploring.
Natural-language travel intent
Destination, date and duration ranking
Optional provider-backed verification
Available now
ADVISOR WORKSPACE

Advisor

Turn a client enquiry into a ranked, bookable shortlist while keeping client context, calendar anchors and discovery together.
Client enquiries and trip briefs
Discovery, verification and shortlists
Calendar-led planning for advisor teams
Available now
DESTINATION & MARKET SHOPPING INTELLIGENCE

Competitive Shopping Audit

See how a destination or travel product performs against realistic alternatives across origin, budget, season and trip length.
Structured travel shopping data
Where the destination wins and loses
Market, budget and season analysis
Coming soon

Faster to valuable options

Narrow the search space before expensive shopping begins, so teams spend time on the combinations most worth exploring.

Higher conversion potential

Keep travellers focused on options that better match the intent, budget and constraints they actually expressed.

Built for real travel data

Discovery can flow into provider-backed verification when price and availability need to be confirmed.

Easy to integrate

Use the API directly in your product or give travel advisors the ready-made workspace for client planning.
One request, end to end

One sentence in. A ranked, verified shortlist out.

Travellers and AI agents describe a trip the way they would say it out loud. MyEscapePlan resolves that intent into ranked candidates, then confirms the ones you choose with provider-backed data.

Traditional search

How a trip is described
A fixed set of fields, filled in and re-submitted until something fits.
What comes back
Pages of results to read, compare and narrow by hand.
What gets shopped
Broad searching across far more of the market than a traveller will ever consider.
What you learn
Queries and clicks, with no record of why one option won.
How competitiveness is tested
Historical analytics: what was searched and booked, read back after the fact.
MyEscapePlan
Discovery + Verified Search

Unlocked

How a trip is described
A sentence in the traveller's own words, understood as stated.
What comes back
A short ranked shortlist, each option carrying the reason it is there.
What gets shopped
Only the candidates worth confirming reach live inventory.
What you learn
Verified outcomes that inform the next plan.
How competitiveness is tested
Controlled traveller scenarios shopped against real alternatives — demand simulation rather than hindsight.
Shopping Intelligence · coming soon
Traveller intent
From London, 4 days next month, under £300pp, somewhere warm with direct flights.
Understood as
London
3–5 nights
£300pp
Direct only
Flexible dates
Ranked shortlist
1
AGP

Málaga

3–5 nights · flexible window
Verified
from £248pp
Example
Strong fit
2
ALC

Alicante

3–4 nights · flexible window
Strong fit
3
PMI

Palma

3–5 nights
Possible fit
Discovery · Available now

Discovery API

Natural-language intent in, ranked and explained candidates out. Synchronous planning that makes no live supplier calls.
POST /api/v1/business/discovery
Destinations, date windows and durations
Reasons and qualitative fit for your UX
Verification · Pilot workflow

Verified Search

Provider-backed checks on the candidates worth confirming, returning live price and availability for the shortlist you actually shop.
POST /api/v1/business/searches
Asynchronous and provider-backed
Scoped and provisioned separately from Discovery
An intent-driven decision layer

Understand the request. Rank the options. Verify the shortlist.

01

Understand intent

Resolve a natural-language request into origin, dates, duration, budget, party and the preferences a traveller actually stated.
02

Rank what is worth shopping

Reason across destinations, date windows, durations, budget and traveller constraints to prioritise the combinations most worth attention.
03

Verify what matters

Confirm price, availability and fit for chosen candidates through provider-backed Verified Search, rather than shopping the entire space.
Evidence loop

Every verified outcome improves the next shortlist.

Provider-backed outcomes and downstream feedback create structured evidence for refining intent understanding, candidate ranking and search strategy over time.
Independent industry evidence

Travel shopping already runs at volumes where the wrong request is expensive.

Look-to-book ratios put many shopping requests behind every booking, and agentic travel adds another source of iterative, automated search as AI travel agents explore and refine options on a traveller's behalf.
1,000–10,000+:1
OTA & metasearch look-to-book
IATA's 2019 scalability study measured shopping requests per booking for OTA and metasearch traffic, the highest-volume shopping channel it examined.
IATA NDC Scalability Study · 2019 ↗
100–300:1
Airline-direct look-to-book
IATA's 2019 scalability study contrasted OTA and metasearch volumes with roughly 100–300 shopping requests per booking on airlines' own websites.
IATA NDC Scalability Study · 2019 ↗
150+
Tool calls in long-horizon agentic travel
TRIP-Bench reports realistic long-horizon travel dialogues that can involve more than 150 tool calls across 18 travel tools.
TRIP-Bench · 2026 ↗
Look-to-book is an industry shopping-to-booking ratio. It is not a claim that a single traveller request produces that many HTTP calls. TRIP-Bench separately reports 150+ tool calls in realistic long-horizon travel dialogues; those calls span travel tools and are not flight-supplier calls alone.
Measured with MyEscapePlan

A smaller shopping space that still holds the right options.

Canonical benchmark · Flights + Accommodation · 4 scenario families · 40 requests · September 2026
67.7%
Mean logical shopping-space reduction
Across the equally sized Flights and Accommodation tracks.
67.5%
Recall@10 retained
Mean across the equally sized Flights and Accommodation tracks.
67.8%
NDCG@10 ranking quality
Mean across the equally sized Flights and Accommodation tracks.
Case studies · Coming soon

Shopping Intelligence case studies are coming soon.

The public case-study library will launch with the Competitive Shopping Audit methodology and benchmark.
Three ways to use MyEscapePlan

Bring MyEscapePlan into your travel workflow.

Use Discovery API in your product, Advisor for client planning, or talk to us about Shopping Intelligence for destination and market analysis.

MyEscapePlan Travel Intelligence

Travel intent infrastructure: natural-language discovery, ranked shortlists and provider-backed verification, plus travel shopping data and market intelligence.
MYESCAPEPLAN LTD · Company no. 16395758 · Registered in United Kingdom
Discovery API and Advisor are available now. Destination & Market Shopping Intelligence is coming soon.
Discovery benchmark figures, their formula and their limitations are published in full.
Read the benchmark methodology ↗
Shopping Intelligence case studies are a separate release, and will publish with the Competitive Shopping Audit benchmark.
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