
Hiring
Cangrade Enhances Jules AI Video Interview Copilot With Structured, Transparent Auto-Scoring And Response Summaries

Enhanced capabilities deliver job-relevant, content-based scoring and candidate response summaries further simplifying Cangrade’s hiring workflows
WATERTOWN, Mass., March 17, 2026 (GLOBE NEWSWIRE) -- Cangrade, the AI candidate screening platform, today announced a new enhancement to its Jules AI Video Interview Copilot: automated video interview scoring and built-in candidate response summaries. The added functionality strengthens Cangrade’s video interviewing solution by making evaluations more structured, efficient, and transparent.
When a recruiter enters a job description into Jules AI Video Interview Copilot, the system identifies the soft skills required for the role and automatically generates a structured scoring rubric for each. For every soft skill identified, Jules outlines clear evaluation criteria and defines what constitutes a strong, acceptable, or weak response. Recruiters maintain full control and can edit or refine any rubric or criteria before interviews are sent to candidates.
After candidates complete their video interviews, Jules generates concise summaries of each response, providing hiring teams with a standardized overview of candidate answers. This functionality responds directly to ongoing customer and prospect demands for faster, more digestible interview insights.
In addition to summaries, Jules evaluates each response against the predefined rubric, scoring answers based strictly on candidate responses. Recruiters can review scores for each individual criterion tied to a question, see how those criteria aggregate into a response-level score, and access an overall interview score that averages performance across all responses.
While automated interview scoring is not a new or novel feature, Cangrade’s approach remains intentionally structured and transparent. Evaluations are limited to job-relevant criteria derived directly from the role, and every score is traceable to clearly defined standards that recruiters can modify at any time.
“Our customers have been asking for structured scoring and response summaries within video interviews, and this enhancement delivers exactly that,” said Gershon Goren, Founder and CEO, Cangrade. “It expands the functionality of Jules Copilot in a practical way, arming recruiters with the insights they need, grounded in job-relevant criteria, while keeping the process easy, personalized, and transparent.”
By combining automated summaries, job-aligned rubrics, and content-based scoring, the enhanced Jules AI Video Interview Copilot helps streamline collaboration among recruiters and interviewers, standardize evaluations, and reduce administrative workload. The result is a more robust video interviewing experience that simplifies hiring workflows.
About Cangrade
For HR leaders, Cangrade is the AI candidate screening platform. By building custom candidate screening flows instantly, Cangrade fuels talent decisions that improve business and employee outcomes throughout the entire talent lifecycle. Delivering 10x more accurate predictions of talent success and retention than traditional methods, the company’s screening solutions have helped organizations like Wayfair, FDNY, Lamar Advertising, and Applied Industrial Technologies make the right hiring decisions for over 10 million candidates and counting. For more information, visit www.cangrade.com.
Media Contact:
Gina Devine
Public Relations
press@cangrade.com
Frequently Asked Questions
What is the new enhancement to Cangrade's Jules AI Video Interview Copilot?
The new enhancement to Cangrade's Jules AI Video Interview Copilot includes automated video interview scoring and built-in candidate response summaries.
How does the AI Copilot generate scoring rubrics?
The system identifies required soft skills from a job description and automatically generates a structured scoring rubric with clear evaluation criteria for each skill.
How does Cangrade ensure transparency in its automated scoring?
Cangrade's approach limits evaluations to job-relevant criteria, derived directly from the role, with every score traceable to clearly defined standards that recruiters can modify.
First published on Wed, Mar 18, 2026
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