RLink Partners

HR PRODUCT

Visualizing candidate track records and working style through AI and data

Snapp Check improves hiring accuracy and helps prevent early turnover and culture mismatch through an AI-enabled online reference check service
SNAPP CHECK

Bringing transparency and objectivity to hiring decisions in a competitive talent market

Reference checks confirm a candidate’s track record and working style through former managers and colleagues They are an effective way to reduce early turnover and culture mismatch risk

  • VisibilityTrack record, character, working style, and peer feedback
  • PurposePrevent mismatch, improve hiring accuracy, and support accountability
  • FeatureAI-based candidate data validation and insight extraction

WHY IT MATTERS

Background

01

Reduce early turnover risk

Confirm a candidate’s track record and working style through third-party perspectives before hiring

02

Hiring process transparency

Objective information makes hiring decisions easier to explain to investors and stakeholders

03

Supplement what interviews cannot reveal

Clarify performance repeatability, collaboration style, and culture fit that may be hard to see during interviews

AI REFERENCE CHECK

Snapp Check Features

01

Candidate Data Validation

Organize evaluation information that can vary across calls and surveys through AI and data

02

Insight Extraction

Analyze reference responses to identify strengths, concerns, and points to verify

03

Fast Information Delivery

Streamline time-consuming checks and add decision inputs without slowing selection

04

Skill Fit & Culture Fit

Provide objective information to assess skill fit and organizational fit more accurately

USE CASE

Use Cases

01Executive

Check candidates from multiple angles for executive roles with broad impact

02Professional

Understand performance repeatability, working style, and team fit for high-impact professionals

03Final Check

Clarify additional questions and points to verify before final interviews

04Process

Standardize information for hiring decisions and reduce overly individual judgment

Deepen candidate understanding and reduce hiring mismatch

We propose usage models based on your adoption purpose and selection process

CONTACT