AI Matcher turns CVs and job ads into structured data, ranks people against roles in any language, and tells your recruiters why each match scored the way it did. One REST API, hosted in the EU or on your own servers.
Positions with dates and employers, education, skills, languages, certifications and contact details, as clean JSON. Values that are not in the document are dropped, never invented.
Rank the candidates in your database for a job, or the open jobs for a candidate. Filter on tags and document ids, and search several databases at once.
Recruiters see which titles, skills and languages matched, and can weight categories their own way. No black box, no hidden rule learned from past hires.
Reads multi-column layouts, scanned pages and old Word files. Strips text that tries to instruct the AI and flags the CV for review.
Pulls the title, required and preferred skills, languages, education and years of experience out of any job ad.
Semantic ranking across your candidate databases, with filters on tags, document ids and custom values.
A language model reads each CV against each job ahead of time and stores a fit score with category scores, ready the moment a recruiter asks.
Fill your create-candidate form with name, email and phone in seconds; the full profile is ready by the time the recruiter clicks save.
Index overviews, rescoring, and a GDPR erase that removes a candidate from every index with a receipt you can keep.
Two ways of scoring work together, so a recruiter never waits for the AI and never gets a half-finished list.
Each CV and job is parsed once and turned into vectors for its full text and for every skill and title.
A match answers in under a second from the stored vectors, across languages, with no model call on the way.
A language model scores every candidate for each active job, closest first, and keeps the scores.
Once every candidate of a job is scored, the list answers with AI fit. Lists are never a mix of the two.
The API speaks the request and response shapes most recruitment software already uses for parsing and matching. Point your existing integration at a new base URL, keep your index and document ids, and move your data across with our migration tools.
POST /v1/matcher/indexes/jobs/documents/job-42
X-Api-Key: aim_••••••••
{
"IndexIdsToSearchInto": ["candidates"],
"Take": 20
}
200 OK
{
"Value": {
"ScoreSource": "fit",
"Matches": [
{ "Id": "cv-1093", "FitScore": 92,
"FitCategoryScores": { "JobTitles": 95, "Skills": 90 } }
]
}
}
The API runs in the EU. Larger customers can run the whole stack, models included, on their own servers.
Parsing and scoring use open models on dedicated GPUs. CVs are never sent to a third-party AI provider.
Every environment's indexes are kept apart. One customer can never search, score or delete another's data.
Remove a candidate from every index, with their scores and queued work, and keep a PDF receipt.
Hidden instructions in a CV are removed before any model reads it, and the CV is flagged.
The AI reads a CV without name or contact details, and scores against the job's requirements only.
Your own CVs and jobs, ranked side by side with your current results.
Start a pilotSend us a sample of CVs and jobs. We run them through AI Matcher and walk you through the results.