Every group chat has the same unanswered question.

'Where should we meet?' stalls conversations in every messaging app. InBetween resolves it in one request: fair on travel, right for everyone's preferences, and decided without leaving the thread.

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Alex, Sam

Free later? Fancy grabbing a drink somewhere central

17:42

Yes! Somewhere quiet with veggie options?

17:43

Meeting suggestion

The Lantern Room

Quiet · veggie-friendly · ££

Alex 8 minSam 9 min
Looks goodMore options

Perfect! 6pm?

17:44

For chat, community, and messaging product teams

The plan should be made where the conversation is.

The pattern is familiar: a group agrees to meet, then the thread stalls. People leave to search maps, screenshots come back, someone points out the first option has nothing vegetarian and the second is miles from the station. The energy drains away.

Every departure from the thread is engagement your platform loses at its highest-intent moment. The group was ready to decide; the tools were somewhere else.

InBetween puts the answer in the thread. Everyone's locations and preferences go into one request, and a fair shortlist comes back for the group to vote on, confirm, and act on natively.

What breaks today

  • Planning scatters into map apps and screenshots the moment it gets real.
  • Reconciling several locations and diets by hand is why plans fizzle out.
  • The highest-intent moment in the chat happens outside your platform.

What you can ship

  • Group decisions made inside the conversation, start to finish.
  • Travel fairness and dietary needs reconciled automatically.
  • More plans that actually happen, attached to your product.

How it works for you

  1. 01

    Catch the planning moment

    Offer venue suggestions when a group starts discussing meeting up.

  2. 02

    Collect light inputs

    Approximate locations and a few preferences are enough; nobody fills in a form mid-chat.

  3. 03

    Return a shortlist to the thread

    Interactive cards the group can vote on and confirm without leaving the conversation.

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.

Group plans, resolved in-thread

Answer 'where should we meet?' inside the chat.

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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