PageUp just launched their big AI play: next-generation skills matching powered by their AI assistant "Paige." The marketing is slick—they're promising intelligent candidate matching, automated skills gap analysis, and AI-powered talent recommendations that "revolutionize how you find qualified candidates."

I've heard this song before. Every ATS vendor is slapping "AI" on their resume parser and calling it innovation. So I did what I always do: I tested it for a month with real hiring needs to see if PageUp's AI skills matching actually works or if this is just expensive vaporware.

Let's talk real talk about what Paige does well and where it falls short.

What PageUp's AI Skills Matching Actually Does

PageUp's platform is built around skills-based hiring, which is trendy right now for good reason. The idea: match candidates based on actual skills and competencies rather than job titles, education credentials, or years of experience.

Paige, their AI assistant, handles several key functions:

Skills extraction and normalization: The AI scans resumes and profiles to identify skills, then normalizes them into a standardized taxonomy. If one candidate lists "JavaScript" and another lists "JS," Paige recognizes these as the same skill.

Candidate-to-job matching: Paige analyzes job requirements and matches candidates based on skills overlap. It ranks candidates by fit percentage and highlights which required skills each candidate possesses.

Skills gap identification: The system identifies where candidates have partial fit—they meet 80% of requirements but lack specific skills. This helps you decide whether a strong candidate with one missing skill is worth developing versus waiting for a "perfect" match.

Talent intelligence and recommendations: Paige supposedly learns from your hiring decisions over time, improving its matching accuracy and surfacing candidates who fit patterns you've historically hired.

Internal mobility matching: For existing employees, Paige can identify internal candidates whose skills align with open roles, supporting internal mobility strategies.

Where PageUp Actually Delivers

I'll give credit where it's due: some aspects of this system work well.

The skills taxonomy is solid: PageUp has built a comprehensive skills library covering thousands of competencies across industries. The normalization works reliably—it correctly identifies when candidates describe the same skills using different terminology.

Skills-based search is legitimately useful: Being able to search your talent pool by specific skills combinations rather than job titles is powerful. "Find me candidates with Python, AWS, and Agile experience, even if they weren't in software engineer roles" is genuinely helpful for identifying non-obvious fits.

The gap analysis feature is smart: Showing candidates who meet 7 out of 10 requirements with clear visibility into which skills are missing helps make informed decisions about near-miss candidates. This is better than the binary "qualified/not qualified" approach most ATSs take.

Internal mobility matching works: If you're serious about internal recruitment, Paige's ability to surface internal candidates with relevant skills is valuable. We identified three internal candidates for roles we were about to post externally.

Where It Falls Short (And Why That Matters)

Now let's talk about the problems, because there are several.

The AI isn't as "intelligent" as marketed: Despite claims of machine learning that improves over time, Paige's matching logic feels more like sophisticated keyword matching than true AI. It ranks candidates based on skills overlap but doesn't deeply understand context, career trajectory, or transferable competencies.

For example: it matched a candidate with "project management" skills to a senior program manager role, but didn't recognize that managing small internal projects is fundamentally different from managing $10M+ strategic initiatives. The skill name matched; the depth and context didn't.

Skills extraction quality is inconsistent: The AI works great with clearly formatted resumes that explicitly list skills. It struggles with resumes where skills are embedded in job descriptions rather than listed in a skills section. Compared to tools like Eightfold or HireVue, PageUp's extraction feels a generation behind.

"Learning from your hiring decisions" is overstated: We didn't see meaningful improvement in matching accuracy over our month-long test. The system doesn't seem to be doing sophisticated pattern recognition—it's applying rules-based logic that remains static.

Integration limitations: PageUp works best as an end-to-end platform. If you're trying to integrate Paige's skills matching with a different ATS or HRIS, you'll hit friction. The skills intelligence doesn't flow seamlessly across systems.

Implementation complexity: Getting skills-based hiring to work requires significant upfront investment. You need to define skills taxonomies for every role, train hiring managers on skills-based evaluation, and restructure job descriptions. PageUp doesn't make this easy—they expect you to figure it out.

Who Should Actually Consider PageUp

PageUp makes sense for:

• Mid-to-large enterprises already committed to skills-based hiring as a strategic initiative
• Organizations with significant internal mobility needs who want to surface internal candidates
• Companies willing to use PageUp's full platform rather than trying to integrate one feature
• Businesses with recruiting ops teams who can handle implementation complexity

PageUp does NOT make sense for:

• Startups or small companies with simple hiring needs and tight budgets
• Companies not ready to fully commit to skills-based hiring (the tool only works if you restructure your process)
• Organizations using other ATSs who just want to add AI skills matching (integration is painful)
• Teams without technical recruiting ops support (implementation requires expertise)

How PageUp Compares to Alternatives

If you're evaluating AI skills matching tools, here's how PageUp stacks up against competitors:

Eightfold AI - More sophisticated AI, better at understanding context and transferable skills. More expensive and complex to implement. Better for large enterprises doing high-volume hiring.

Phenom - Stronger on candidate experience and personalization. Skills matching is good but not as deep as PageUp. Better if CX is your priority.

HireVue - Focused on assessments and interviews with solid skills validation. Different use case than PageUp's full-platform approach.

Workday - If you're already on Workday, their skills matching is integrated and adequate. PageUp is better if you're platform-agnostic.

The Pricing Reality

PageUp doesn't publish pricing publicly, which is always a red flag for expensive software. Based on conversations with their sales team and industry benchmarks, expect:

• Mid-market pricing: $50,000-$150,000 annually depending on headcount and modules
• Enterprise pricing: $150,000+ annually for large organizations
• Implementation costs: Additional fees for setup, integration, and training

That's not outrageous for enterprise recruiting platforms, but it's real money. For that investment, you need to be absolutely certain you'll use skills-based hiring comprehensively—not just experiment with it.

The Bottom Line

PageUp's AI skills matching with Paige is solid—not revolutionary, but solid. The skills taxonomy is well-built, the gap analysis feature is useful, and internal mobility matching works well. If you're committed to skills-based hiring and willing to invest in implementation, PageUp can deliver value.

But it's not magic. The AI is more sophisticated keyword matching than true intelligence. Skills extraction quality is inconsistent. And the "learns from your hiring decisions" claim is overstated—we didn't see meaningful improvement over time.

If you're already evaluating skills-based hiring platforms and you have the resources to implement properly, PageUp belongs on your shortlist. But if you're expecting plug-and-play AI that instantly transforms your recruiting with zero effort? Look elsewhere.

Don't buy tools because they have "AI" in the marketing. Buy them because they solve real problems at a price that makes sense for your business. PageUp can solve skills-based hiring problems—but only if you're actually ready to commit to skills-based hiring.

PageUp Pros:

• Solid skills taxonomy and normalization
• Useful gap analysis showing near-miss candidates
• Strong internal mobility matching
• Skills-based search is legitimately helpful

PageUp Cons:

• AI matching less sophisticated than marketed
• Skills extraction inconsistent with non-standard resumes
• Implementation requires significant investment
• Integration challenging outside PageUp ecosystem
• Expensive for what it delivers

Bottom line: Worth considering if you're committed to skills-based hiring at scale. Not worth it if you're just experimenting or need simple resume screening.