Overview
Discovery API
Developers
Shopping Intelligence
Case studies
Pre-shopping intelligence for travel

Make fewer, better travel searches.

MyEscapePlan ranks the destinations, dates and trip lengths worth shopping before live inventory requests are made — reducing unnecessary downstream API calls while preserving relevant travel options.
TRAVEL DISCOVERY INFRASTRUCTURE

Discovery API

Reduce travel search fan-out by turning vague intent into a constrained, ranked shopping plan before expensive live inventory APIs are called.
Natural-language travel intent
Reduce unnecessary downstream API calls
Destination, date and duration ranking
Available now
DESTINATION & MARKET SHOPPING INTELLIGENCE

Competitive Shopping Audit

Turn controlled travel shopping data into evidence about how a destination or travel product performs against realistic alternatives.
Structured travel shopping data
Where the destination wins and loses
Origin, budget, season and trip-length analysis
Coming soon
Independent industry evidence

Travel shopping already operates at volumes where fewer API calls matter.

IATA's 2019 scalability study reported OTA and metasearch look-to-book ratios of roughly 1,000–10,000+:1 shopping requests per booking. 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 reported roughly 1,000:1 in best cases and commonly above 10,000:1 for OTA and metasearch shopping.
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, not a claim that one traveller request creates 1,000–10,000 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.
An intelligent decision layer

Select what to shop. Learn from every result.

01

Understand intent

Parse origin, dates, duration, budget and flexible traveller preferences.
02

Intelligently select what to shop

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

Observe real outcomes

When provider-backed verification is used, capture price, availability and fit without brute-forcing the full search space.
Evidence loop

Every verified outcome can improve the next shopping plan.

Provider-backed shopping outcomes and downstream feedback create structured evidence for refining candidate selection, ranking and search strategy over time.
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.
Available now

Start with a Discovery API pilot.

A 4–6 week pilot measures integration quality, recommendation quality and shopping-space reduction before a larger production commitment.

MyEscapePlan Travel Intelligence

Travel discovery infrastructure for reducing search fan-out, plus travel shopping data and market intelligence.
MYESCAPEPLAN LTD · Company no. 16395758 · Registered in United Kingdom
Discovery API is available now. Destination & Market Shopping Intelligence is coming soon.
Shopping Intelligence case studies will publish when the benchmark and methodology are ready for public release.
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