Transparent AI scoring with human control

An AI candidate match score is a structured way to measure how well a candidate meets the requirements of a specific role. In Pinpoint, the score is based on a set of clearly defined criteria, such as “people management experience” or “B2B SaaS experience.” Each candidate is assessed against those criteria and given a score based on how many they meet.

The score is not designed to replace recruiter judgment. Instead, it acts as a starting point for review, especially when application volumes are high. Recruiters can quickly see which candidates meet the core requirements and focus their time on the most relevant applications first, without having to read every resumé in full.

In Pinpoint, AI candidate matching starts with defining what “good” looks like for the role. The platform suggests criteria based on the job description, or recruiters can write and refine them manually. These criteria are typically simple, role-specific signals that reflect how screening decisions are actually made.

Once the criteria are set, the AI evaluates each candidate’s application, resume, and any additional answers against them. The result is a score showing how many criteria were met, along with a breakdown explaining each score. This makes it easy to understand how the score was reached and to adjust criteria if needed.

Good criteria are specific, relevant to the role, and easy to evaluate consistently. Instead of vague signals like “strong communication skills,” effective criteria focus on observable experience, such as “has managed stakeholder relationships” or “has used Salesforce.”

In Pinpoint, criteria can be generated from the job description and then refined by the recruiter. This keeps the process grounded in the actual requirements of the role, rather than relying on a generic or pre-trained model. 

Accuracy depends on two things: the quality of the criteria and the quality of the candidate data. When criteria are clearly defined and aligned to the role, the AI can reliably identify whether those signals are present in a candidate’s application.

Pinpoint supports this by showing a breakdown for every score, so recruiters can see exactly what was matched and why. If something is missed or misinterpreted, the criteria can be adjusted. This feedback loop helps improve accuracy over time and keeps recruiters in control of the process.

AI candidate matching is best used to support, not replace, manual screening. It helps teams prioritize which candidates to review first, but it doesn’t make final hiring decisions.

In practice, it reduces the time spent scanning applications and brings consistency to early-stage screening. Recruiters still review candidates, interpret context, and make decisions, but they start from a more structured and informed position rather than working through applications sequentially.

When application volumes are high, recruiters rarely review every candidate under the same conditions. Timing, fatigue, and workload all influence who gets seen first. This can lead to inconsistent outcomes and missed candidates.

AI candidate scoring addresses this by applying the same criteria to every application, regardless of when it’s reviewed. Instead of working through candidates in order of when they applied, teams can immediately identify which applicants meet the key requirements and focus their attention there. This makes early-stage screening more consistent and scalable.

Keyword filtering relies on exact matches, which can miss relevant candidates if they use different wording or phrasing. It also doesn’t provide any explanation beyond whether a keyword was found.

AI candidate matching evaluates meaning and context, not just keywords. It looks at whether a candidate meets a defined criterion, even if the wording varies. In Pinpoint, this is combined with a clear explanation for each result, so recruiters can see why a candidate was or wasn’t considered a match.

AI candidate scoring is most useful at the top of the funnel, during initial screening. This is where application volumes are highest and where inconsistency is most likely to affect outcomes.

By providing a structured way to assess every candidate against the same criteria, it helps teams prioritize effectively before moving into deeper evaluation stages like interviews and scorecards. It becomes a foundation for the rest of the hiring process, rather than a standalone decision tool.