Recommendations that feel like a local made them.

InBetween matches travellers with restaurants, bars, and cafés on the things they actually care about: dietary needs, atmosphere, budget, and a walk they will enjoy.

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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 travel guides, tourism boards, and city apps

Tourists do not want a map. They want a friend who lives there.

Drop a traveller into an unfamiliar city with a map full of pins and you have given them homework. What they wanted was the thing a local friend provides: three good options that suit them, near where they are.

That judgement is data: dietary coverage, atmosphere, price, quality signals, and walking distance, matched against what this particular traveller cares about.

InBetween supplies it through one API, so your guide can make confident, personal recommendations at a scale no human curation team could match.

What breaks today

  • A map of pins hands the research work to the traveller.
  • Dietary and accessibility needs are hardest to satisfy in an unfamiliar city.
  • Travellers leave the guide to cross-check every suggestion elsewhere.

What you can ship

  • Suggestions personal enough that travellers stop second-guessing them.
  • Dietary, budget, and distance constraints respected by default.
  • A guide travellers keep using for the whole trip.

How it works for you

  1. 01

    Capture preferences once

    Dietary needs, atmosphere tastes, and a comfortable walking radius become part of every suggestion.

  2. 02

    Match against deep local data

    Structured venue attributes do the work a local's judgement would.

  3. 03

    Slot recommendations into itineraries

    Ranked suggestions fit naturally into day plans, neighbourhood guides, and 'near me now' features.

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.

Travel recommendations, done properly

Give every traveller a local's shortlist.

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

Start in minutes with the knolage you can scale without stress.

Generous free usage to evaluate, startup credits to grow, and a migration path when you are ready to switch from stacked Places pipelines.

Start in minutes

No sales call. Sign in, grab a key, and ship a first search.

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£10 of usage every month, full core API, no card required.

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£2,500 usage credit, Grow-level features, and onboarding support.

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Migration credits from £500, dual-running help, and high-spend matched offers.

Migration programme