Concierge judgement, available at every scale.

A great concierge knows who wants a quiet courtyard and who needs proper gluten-free options. InBetween encodes that judgement as structured data your digital channels can query.

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Exploring

Lisbon · Alfama

Vegan-friendly
Neighbourhood cafés
Local vibe
Walkable
api.inbe.us/v1/search
POST
{
"locations": [{ "locationName": "Alfama, Lisbon" }]
"mode": { "mode": "POINT", "maxDistance": 500 }
"preferences": { "catersFilters": ["vegan"], "goodFor": ["local"], "chainPreference": "PREFER_INDEPENDENT" }
}
Matched to 3 taste profiles · near hotel
Feels locally curated

For hotels, concierge platforms, and luxury travel brands

The concierge does not scale. The judgement can.

What makes a great concierge is specificity: the tranquil courtyard for one guest, the reliably gluten-free kitchen for another, the pleasant ten-minute walk from the lobby for a third. That knowledge has always been trapped in one person's head, available to whoever reaches the desk.

InBetween encodes it. Atmosphere, noise, dietary coverage, walking distance, and dozens of quieter signals are structured attributes your guest app, messaging channel, or in-room tablet can query.

Every guest gets the considered recommendation, in every property, at any hour, and your concierge team is freed for the requests that genuinely need a human.

What breaks today

  • Digital concierge tools default to the same top-ten list for every guest.
  • Complex dietary requirements are handled manually or not at all.
  • Guests go to outside apps when in-house suggestions feel generic.

What you can ship

  • Concierge-standard recommendations across every digital channel.
  • Dietary, atmosphere, and distance preferences honoured consistently.
  • Guests who trust, and use, your in-house recommendations.

How it works for you

  1. 01

    Carry guest preferences into every query

    Dietary requirements, atmosphere tastes, and walking comfort shape each suggestion.

  2. 02

    Return shortlists worthy of the desk

    Small, considered sets of venues, not pages of nearby results.

  3. 03

    Serve every digital touchpoint

    Guest messaging, in-room tablets, and booking apps all draw on the same intelligence.

Every venue. Every detail.

Precision metadata for apps that help people meet, eat and drink.

Venue intelligence

Ultra detailed venue data

Get detailed information about venues, including menus, pricing, ambiance, and more.
Smart matching

Preference driven

Wow your customers with recommendations and information tailored for them.
Multi-location search

Tools for multiple locations. Find restaurants, bars and cafés based on multiple sets of preferences and locations.

In-app experience

Loyal to you

Keep users inside your app by giving them the info they need to make decisions.
Decision support

Built for decision-making

A suite of tools designed to enable you to answer customer needs directly.

Digital concierge intelligence

Give every guest the considered recommendation.

Open up a new world for your users with InBe's preference-aware venue discovery—built for group venue matching, dietary search, and ranked shortlists.

No card required. Full core API access.

Start building for freeTalk to us

Keep exploring

Questions teams ask

Built for developers who connect people to places

Start buildingExplore the API

Connecting users to real-world places shouldn’t mean stitching geocoding, routing, Places search, and ranking yourself. One preference-aware search replaces the pipeline.

api.inbe.us/v1/search200 OK
POST
{
"locations": [
{ "lat": 51.523, "lon": -0.158, "label": "Alex" },
{ "lat": 51.493, "lon": -0.098, "label": "Sam" }
],
"mode": { "mode": "POINT", "maxDistance": 750 },
"preferences": {
"goodFor": ["date", "cocktails", "conversation"],
"catersFilters": ["vegetarian"],
"establishmentFilters": ["servesCocktails"]
}
}
response
{
"centerPoint": { "Lat": 51.508, "Lon": -0.128 },
"radius": 750,
"establishments": [
{
"displayName": "The Lantern Room",
"distanceMeters": 142,
"matchScore": { "total": 0.94 }
},
{
"displayName": "Barrio Soho",
"distanceMeters": 186,
"matchScore": { "total": 0.91 }
}
]
}

Fair middlegrounds

Multi-person locations in one call; we balance travel so you don’t build routing yourself.

Preferences that actually match

Dietary, vibe, good-for, and constraints as first-class inputs, not post-filters on pins.

Pay for the hard parts

Core search and enriched place data stay cheap; you pay where decision intelligence earns its keep.

Tools for user decisions

Ranked shortlists and experience-ready answers so your product keeps users in-flow, not off to a map.

Illustrative scenario

0ms

Ranked shortlist in one call

Preference-aware search returns a decision-ready shortlist without stacking Places, routing, and enrichment calls.

~15× faster vs multi-call pipelinesNot a live benchmark
RESTWorks with any stack
curl -X POST https://api.inbe.us/v1/search \
  -H "API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "locations": [
      { "lat": 51.523, "lon": -0.158, "label": "Alex" },
      { "lat": 51.493, "lon": -0.098, "label": "Sam" }
    ],
    "mode": { "mode": "POINT", "maxDistance": 750 },
    "preferences": {
      "goodFor": ["date", "cocktails", "conversation"],
      "catersFilters": ["vegetarian"],
      "establishmentFilters": ["servesCocktails"]
    }
  }'
Full examples in docs

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