AI Candidate Sourcing Agent

Keywords match exact terms – Python matches Python. Semantic search understands meaning – it knows a data engineer with Spark experience is relevant for a machine learning pipeline role even if those words never appear together.

Yes. The agent automatically searches past applicants who made it to final rounds but were not selected. It re-evaluates them against current roles, factoring in any new skills or experience they have gained since.

Yes. The agent searches across internal employees, past applicants in your ATS, silver medalists, and referral networks – presenting the best candidates regardless of source.

ATS keyword search matches exact terms. Gloat's agent uses vector embeddings to find candidates with relevant skills even when their profiles use entirely different terminology. The result is a candidate pool three times larger than keyword search produces from the same database.

Yes. Managers describe the person they need in plain language through Microsoft Teams or Slack. They might type "I need someone who can build data pipelines, has experience with cloud infrastructure, and works well with a small engineering team." The agent interprets that description, runs the search, and returns a ranked shortlist with fit analysis. No Boolean strings, no ATS navigation, no training required.