# AI candidate matching

> Define match criteria, run matching, and work the ranked shortlist — with per-criterion evidence for every candidate.

_Collection: Jobs & Pipeline (jobs-pipeline) — Kepler Help Center. Canonical: https://keplercrm.com/support/articles/ai-candidate-matching/_

Kepler builds a ranked shortlist for each job from your existing candidate database, scored against criteria you control. It lives on the job's **Matching** tab.

## Match criteria — the rubric

Matching is **criteria-first**: the job carries a rubric of requirements, and every candidate is judged against each one.

  - Click **Match criteria** on the Matching tab. On first run, Kepler **generates criteria automatically** from the job brief — review and edit them before trusting the results.

  - Each criterion is a plain-language requirement (e.g. *"5+ years building backend services in Rust"*) marked **Must-have** or **Nice-to-have**. Manage **Search keywords** separately below the rubric; these short terms widen the pool of candidates retrieved for scoring.

  - Add, edit or remove criteria any time — the next run scores against your edited rubric.

## Running matching

Click **Run matching** (**Re-run matching** once results exist). Kepler retrieves a candidate pool using semantic and keyword search over profiles and CVs, then an AI judge assesses each candidate against your criteria across skills, seniority and domain. The run continues in the background with a progress banner; when it completes, review the results.

## Reading the results

Results are grouped into **Suggested**, **Accepted** and **Rejected** sub-tabs, viewable as a **board** (columns Strong → Good → Weak → Off spec) or a full **list**.

| Column | What it shows |
| --- | --- |
| Match Strength | The judge's verdict: Strong , Good , Weak or Off spec . Open a candidate's evidence inspector to see the percentage score and supporting assessment. |
| Criteria | A chip per criterion: Met (green), Partial (amber), Missing (red) or Not stated — hover for the evidence behind each verdict. |
| AI Assessment | The judge's written reasoning for this candidate. |

Match strength is set by the AI judge and can't be dragged between columns — you act on it instead: **Accept** a suggestion to add the candidate to the job's pipeline, or **Reject** it (with an optional reason). Re-run matching to re-score after the brief or criteria change.

## Improving match quality

  - **Edit the criteria.** The rubric is the single biggest lever — make must-haves genuinely non-negotiable and move preferences to nice-to-have.

  - **Keep CVs current.** A candidate's assessment reflects their parsed profile — upload their latest CV and re-parse.

  - **Complete the job brief.** Criteria generation and retrieval both read the description and details; thin briefs produce generic rubrics.

> **NOTE:** Matching never moves candidates or contacts anyone — it only ranks and explains. You decide who enters the pipeline.