# 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 **Criteria** on the Matching tab (the button also shows the number of criteria when a rubric exists). 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** 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.

Running matching and editing the criteria need permission to edit the job. Anyone who can add candidates to a pipeline can accept a suggestion.

## 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**.

| Where | What it shows |
| --- | --- |
| Match column | The percentage score and the judge's verdict: Strong , Good , Weak or Off spec . |
| AI assessment column | The judge's written reasoning for this candidate. It's hidden by default: add it as a column, or open the candidate to read it in full. |
| The candidate's evidence card | Click a candidate to open it beside the list: the Assessment and each criterion marked Met , Partial , Missing or Not stated , with the evidence behind each verdict. |

Match strength is set by the AI judge and can't be dragged between columns — you act on it instead: in the evidence card, **Accept to pipeline** adds the candidate to the job's pipeline, and **Reject…** declines the suggestion (with optional reasons such as Too junior or Location). Re-run matching to re-score after the brief or criteria change.

## How must-haves affect the score

Must-haves are enforced, not just weighed:

  - A candidate with any must-have marked **Missing** is rated **Weak** at best, with a score under 50%.

  - A candidate with any must-have marked **Partial** is rated **Good** at best, with a score under 75%.

  - A must-have marked **Not stated** doesn't lower the score — a profile that doesn't mention something isn't proof the candidate lacks it. The Match column flags how many must-haves weren't confirmed, so you know what to check before you submit.

Nice-to-haves inform the score, but a missing nice-to-have never caps it.

## After changing the criteria

**Criteria edited since this run** means the results were scored before your latest rubric changes. Review the criteria, save your edits and click **Re-run** to assess the candidate pool again. Re-running preserves your **Accepted** and **Rejected** decisions; it does not move reviewed suggestions back into Suggested. Scores and evidence can refresh for candidates assessed again.

## 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.