Alliance for Intelligence Travel: hotel and room mapping, matching

Predictive Hotel Id matching API

beta program. Free access
NDA less
- Standardized standalone API
- No data stored
- No database needed
- No third parties dependencies
- Transparent funnel

How it works

An anonymous standalone.

Our ubiquitous predictive logic can receive any data type, across X number of arguments.
Predictive mapping works as a standalone, down to it s core. The same ubiquitous algorithm is applied for any data.
By using a NLP dual trust scoring model, it can match ids on various scales and refine his trust judgment. see below

check demo sandbox tab to see how it works.

Dual trust authority

Standardized standalone

Arguments used for matching:
name, adr, city, cntry, zip, iso2, desti, other

wysiwyg approach:
What you send is what you get

You can pass any number of arguments as you want, independently per line, for both the input and versus.

Using at least a name or an address argument is best practice. more arguments means a more precise result.

Transparent funnel. No suppliers or ids needed. results are returned as they are sent.

Dual trust authority

2 matching % results are returned:

The trust % is the match trust % based on each argument weight and score.
This is an holistic analysis across all arguments, precisely reflecting tiny differences.

case study: You have a resort on an island, same name, same address, with 3 aisles (north, south, east), having different amenities or star rating. You do not want to consider them as duplicates, despite same address, name, gps...

The confi % is the duplicate confidence ratio based additional correlative algorithms, amplifying the trust ratio curve (positively or negatively).

This allows to find out of the bat more direct matches and duplicates.

case study: You want to sort out duplicates, match ids, qualify sub groups, chains, rentals. Additional correlative algorithms are applied to sort them out.

Our dual trust model for reliability.
Precision for unique & refined view to match duplicates.

APIs scoring NLP model

In Natural language Processing (NLP) a weight defines the importance of an argument. Those weights are used for scoring. The scoring defines the % of dissimilarities.
In addition to the usual arguments, you have at your disposal 2 more (desti & other) for your own purposes (criteria, filters...) Use them in accordance to the weight you want to allocate them. Check below weight table

argument description weight Optional
hid your reference
name The hotel name heavier
adr The hotel address heavier
city The city name average
cntry The country name light
zip The postal code heavy
iso2 The iso 2 country code average
desti Destination or alike, other light
other Your own, can be anything heavy

Use Demoversus credential to try it free.

Copy / paste your own json into the sandbox string box, or use a demo file, and click send to predictive API button below.


Look for trust & confi

Trust is the statistical score

Confi is the match confidence score

Any Confi over 80% is a match

Any Confi under 80%, reject at risk insufficiently qualified response

Enforces 100% mapped hotels coming from any mapping services.

Ajax example below


const request = $.ajax({type: "POST",datatype: "text",url: ``,data: queryJSON});


request.done(response => {responseJSON});



Note the 2 returned matching % arguments, trust and confi.