AI Mobile Context
What AI Can Learn About Your App Through Blue Jetty
See how selected app details help AI assistants explain relevant experiences and offer a publisher-approved next step, using fictional Swell Atlas.
App experience → AI-readable context
Help AI assistants explain what your app offers.
Blue Jetty turns the app's information hierarchy into a compact invitation: what exists, which attributes support a match, and where to send the user. The live forecast, complete map, proprietary guidance, and ongoing interaction remain inside Swell Atlas. This historical example intentionally adds named entities; start with a compact capability and experience map when that is enough.
Catalog
Selection{
"app": {
"name": "Swell Atlas",
"platforms": ["ios", "android"]
},
"inventory": {
"surfSpots": 8,
"trainingSessions": 4,
"guidesAndForecasts": 5,
"events": 3
},
"entity": {
"id": "break-pelican-sands",
"name": "Pelican Sands",
"nativeType": "surf_spot",
"selection": {
"skillLevel": "beginner",
"breakType": "beach_break",
"typicalWaveHeight": {
"value": 2,
"unit": "foot"
},
"preferredTide": "low_to_mid"
},
"handoff": "universal_link"
}
}Selected app details help an assistant explain the fit and find a destination. The complete product experience stays in the app.
A mobile app can offer useful experiences that are difficult to explain in a short store listing. An AI assistant researching the app may find its name, category and broad purpose, while the details that make it relevant to a particular person remain scattered across the publisher's pages and the app itself.
That gap matters when someone asks a specific question, such as “Which app can help me plan beginner surf sessions?” The assistant needs enough context to explain the fit and a useful destination for the person to follow.
Blue Jetty ran a controlled experiment with Swell Atlas, a completely fictional surfing app. We submitted 20 synthetic in-app records: eight surf spots, four training sessions, five guides and forecasts, and three community events. Each record carried a stable ID, short selection summary, source time, availability, typed attributes, and ordered iOS, Android, and web destinations.
This example illustrates the additional context an assistant can retrieve: what Swell Atlas offers, which experience fits and how to reach it. The complete forecasts, live conditions, maps and local guidance stay inside the app. A compact map of major experiences is the starting point; these selected records demonstrate optional enrichment.
Give the assistant a concrete reason to recommend the app
An app-store listing can already explain Swell Atlas's spot guides, planning tools and conditions information. Blue Jetty adds explicit, addressable selection context and publisher-approved routes. The compact before/after example starts with one experience map; this historical study deliberately adds named-entity enrichment.
With these records, an assistant could distinguish a fictional beginner beach break from an advanced reef break, or explain that the app includes a 30-minute paddle-endurance workout. The selected attributes and relationships help it describe what a person can explore in the app. Representative conditions in this test are synthetic, rather than live surf advice.
Those capabilities come directly from making the source facts addressable and comparable.
The full Swell Atlas technical case study documents the questions, evidence, public retrieval surfaces, and isolation controls end to end.
What becomes visible
Four categories of information become materially easier to retrieve.
First is app coverage: the assistant can identify the kinds of experiences available and selected examples that make the app useful.
Second is selection context: typed values and explicit units let a consumer compare records without scraping meaning out of inconsistent prose. A 30 minute duration, beginner skill level, or 16 second swell period is both human-readable and machine-comparable.
Third is authority and time: the record can say what the publisher asserted, when the source changed, when Blue Jetty observed it, and which assessments belong to Blue Jetty. That makes an evidence-bounded answer possible.
Fourth is the next step: the selected experience has a publisher-approved destination. The current app map can prefer an existing MMP link, with native, store or web fallbacks where appropriate. The assistant can include that link with its explanation.
The intended result is an informed next step: “This app contains a relevant experience, here is why it fits, and here is where to explore it.” A person who follows the link can see the value inside the app, with routing handled by the publisher's existing setup.
Why HTML and structured data both matter
The experiment projected the same normalized meaning through public app/entity pages, JSON-LD, JSON and JSONL feeds, search, and sitemaps. That gives different consumers efficient shapes without creating multiple conflicting sources of truth.
Google’s structured-data guidance explains how structured data supplies explicit clues about page meaning. Blue Jetty applies that idea to app context: visible pages carry JSON-LD, while feeds and APIs support retrieval and synchronization.
During the controlled window, representative Swell Atlas pages returned HTML and JSON-LD in synthetic request tests. For a real publisher, the website's reference gives an assistant a direct path to the hosted map. The stack diagram shows how that context can become part of an answer and a preferred-link handoff.
Publish the context that makes your app relevant
An app-store page explains the app and provides an installation destination. Blue Jetty connects publisher-approved app context to relevant experiences, offers and preferred handoffs. This experiment uses selected structured records to demonstrate that richer context; a full catalog is not the default requirement.
That means stable entity identity across updates, explicit deletion behavior, publisher provenance, freshness context, typed vertical details, relationships, and native/web routing. Publish the details needed to recognize the fit and reach the experience. A surf app can describe its coverage and the attributes available for each spot while keeping complete live reports and guides inside the app.
For an app team, the practical advantage is maintaining the experiences, offers and preferred links together in one publisher-authorized app map, with a stable reference from the official website.
A clean path from sample to production
After the experiment, Blue Jetty retained Swell Atlas as owner-visible sample data and withdrew it from public app discovery. The sample supports testing of updates and routing while real app publication follows the publisher-authorization process.
The business-level case study remains public and unmistakably labels the experiment as synthetic. That lets developers inspect the method without presenting a fictional catalog as a real recommendation source.
The private sample gives teams a reference for testing preparation, updates and routing before publishing their own authorized app context.
The new opportunity
When an AI system discovers an app through Blue Jetty, it receives data designed to be useful immediately: compact, current, attributable, synchronized, and routable.
With a publisher's structured map, an assistant can retrieve a specific candidate, explain which attributes support the match, distinguish claims from assessments, and carry the selected destination into the handoff. A website can expose similar facts; the packet makes their structure, provenance and routing relationship explicit.
Start with the experiences that make the app worth trying and the links that help a potential user reach them. Your developer or coding agent can use the setup guide to publish that context.