Job matching is the automated process by which software compares candidate profiles with open positions and produces a ranked list of likely fits. Traditional job boards rely on keyword filters; modern platforms increasingly use semantic approaches that understand synonyms, related responsibilities and industry context.
Under the hood, semantic matching usually relies on vector embeddings. Both the job posting and the candidate profile are translated into multi-dimensional numeric vectors whose proximity reflects content similarity. This allows the system to surface postings that use different vocabulary for the same role – for example "social worker" and "social pedagogue".
Good job matching considers much more than skill keywords: location preferences, weekly hours, presence of a collective bargaining agreement, remote share and industry are weighted alongside softer signals derived from a CV. Generative AI now also makes it possible to translate cover letters and free-text descriptions into machine-readable structured data.
For candidates, this changes the search dynamic. Rather than scanning hundreds of postings by hand, the system delivers a curated short list. Lunigi takes this idea further: a profile is created once, and an AI bot researches matching, future-proof roles every day and ships them as a compact email digest – no portal-hopping required.