The match is your product. The date is the outcome.

InBetween finds first-date venues that are fair on travel for both people and right for the occasion: quiet enough to talk, easy to reach, and mindful of both sets of preferences.

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Alex & Sam

Fair middleground picks

For dating application product leaders

Where to meet is the most delicate question you leave unanswered.

Two people match, the conversation goes well, and then comes the awkward part: someone has to suggest a venue. Too far, too loud, too formal, nothing they can eat. It is a small decision carrying a lot of weight, and right now your product leaves them to face it alone.

InBetween resolves it in one request. Both people's locations, both sets of preferences, and the occasion go in; a fair, comfortable shortlist comes out, ready to share in the chat.

Helping people meet well is not a side feature for a dating product. It is the outcome everything else exists for, and being genuinely useful at that moment is something members remember.

What breaks today

  • Choosing a first-date venue is stressful, and apps leave members to it.
  • Generic place data misses what matters on a date: noise, atmosphere, dietary needs.
  • Planning moves to other apps, and the product loses its most meaningful moment.

What you can ship

  • First-date suggestions that suit both people, offered at the right moment.
  • Planning that stays in your chat instead of scattering to maps and search.
  • Members whose dates go well, which is the retention story that matters.

How it works for you

  1. 01

    Combine both members' preferences

    Dietary needs, noise tolerance, and vibe for two people, handled in one query.

  2. 02

    Find somewhere fair for both

    Travel-time fairness means neither person crosses the city while the other walks five minutes.

  3. 03

    Suggest venues inside the chat

    Date suggestions render as cards in the conversation, where the plan is actually being made.

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"]
}
}
response
"establishments": [{
"displayName": "The Lantern Room"
"distanceMeters": 142
"matchScore": { "total": 0.94 }
"catersFlags": ["vegetarian"]
}]
Profile
Profile

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.

First dates, handled well

Help your matches meet somewhere right.

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 freeExplore the API

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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