Phenom vs Relevance AI

Uses AI to match alumni with open roles using their skills and history.

Automatically logs recruiter actions and AI decisions for audit review.

Lets candidates book interviews against live calendars.

Uses AI to draft career paths between organizational roles.

Uses AI to match contractors to roles in the CRM.

Uses AI to analyze workforce career activity and match employees into talent pools.

AI matching engine that ranks candidates against a role on skills, experience, title, location and engagement, explains the factors behind each score and keeps learning from candidate outcomes and recruiter feedback.

Matches internal employees to short-term projects based on skills.

Uses AI to draft screening questions.

Scores applicants against role criteria for hiring managers.

Uses an AI ontology to match applicants to suitable roles.

Transcribes interviews and generates role-aligned summaries.

Matches employees to mentors using skills, experience, and career goals.

Uses agents to coordinate onboarding tasks.

Shows managers employee skills, engagement, sentiment, and progress toward goals.

Uses an agent to guide new hires through preboarding.

Ranks recruiting-event attendees against event roles.

Matches employee referrals to open roles.

Uses AI to match employee skills and experience to succession-role requirements.

Uses AI to rank candidates against a role.

Provides AI tools for talent marketers.

Matches employees to internal roles, gigs, mentors, and learning based on skills.

Uses AI to rank students against roles attached to campus events.

Uses AI to derive employee skills from workforce records.

Uses AI to suggest role-specific screening questions.

Searches internal and external candidate pools from a natural-language prompt.

Analyzes video, audio, and conversation for identity fraud.

Conducts and scores candidate interviews for recruiter review.

Automates candidate status updates, scheduling, and answers to frequently asked questions.

Turns hiring-manager conversations into job descriptions and sourcing criteria.

Finds candidates and drafts personalized recruiting outreach.

Answers candidate questions using the company's knowledge base.

Employee-facing AI agents that keep talent data current: a Profile Builder that interviews employees, a Skills Validation agent, a Career Coach that answers career questions from skills data, and a Workforce Planning agent that forecasts needed skills.

Analyzes people data to explain hiring outcomes and recommend actions.

Voice Screening Agent conducts candidate screening conversations by phone or browser and provides recruiters with rubric-based scores and transcripts.

Chat app on web, desktop, iOS and Android where a team @-mentions its own Relevance AI agents and workforces in one conversation, picks the language model per chat, and uses built-in agents for deep research, image generation and slide decks.

Researches accounts, produces reports, and updates CRM records.

Clara organizes data into topic clusters and identifies pillar topics and subtopics.

Provides qualitative enrichment and classifies job titles into job functions.

Finlay produces an SEO-optimised FAQ section from a search query.

Kira identifies keyword opportunities and generates prioritized keyword acquisition lists.

Nate sends a meeting bot and returns a video recording and transcript.

Pia creates structured documentation for business processes, workflows, and standard operating procedures.

Relevance Knowledge Manager adds, searches, updates, and deletes records in knowledge tables.

Reva creates an SEO content refresh plan using competitor content gap analysis.

Handles customer support messages using Intercom.

Tala analyzes candidate information against hiring criteria.

Tim generates structured user guides and technical documentation.

Cat bulk-categorizes free-text data and labels rows.

Viola generates videos from images.