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Resumes hide good candidates and interviewing everyone is too costly – this YC startup tests real skills with work simulations first.
ENTRY ANGLES
Embed job simulations inside university courses and training programs to create passive candidate pipelines · Build simulation-based assessment platforms for skills-based hiring evaluation · Create personalized learning platforms that identify individual strengths and adapt learning paths
VERTICALS
CAPABILITIES
AI-powered simulation technology at scale, Integration capabilities with educational institutions, Individual skills assessment and personalization algorithms
Skillfully promises companies they can make hiring decisions "with 100% accuracy."
The pitch: traditional resumes surface only shallow signals – formal credentials, previous employers, listed skills. The information that actually predicts job performance lives beneath the surface, inside the candidate – which is why companies conduct interviews in the first place. But interviews are slow, expensive, and inconsistent.
The problem has gotten worse on two fronts. First, candidates now use AI to polish their resumes to the point where the output reflects their ability to prompt an AI, not their professional capabilities. Second, rising turnover means companies are processing more candidates than ever – and either making more decisions based on these inflated resumes, or spending more time on interviews they can't afford.
Skillfully's solution: a platform for building work-situation simulators that can be inserted directly into the hiring funnel. These simulations accomplish two things simultaneously:
- Surface candidates with the right skills, regardless of their resume. - Filter out candidates without those skills, regardless of how good their resume looks.
Building a simulator requires selecting the competencies required for the role, choosing appropriate simulation scenarios from the platform's library, and sending candidates a link through the company's existing ATS (Application Tracking System). Once candidates complete the simulation, results are automatically appended to their ATS profiles, available for any stage of the process.
The platform currently supports simulations for: project manager, business analyst, marketing coordinator, market researcher, content creator, retail banking associate, investment banking analyst, banking analyst, strategy consultant, business and technology analyst, cybersecurity specialist, audit associate, research auditor, and several adjacent roles.
Skillfully has now raised a meaningful $2.5M seed round following a couple of small angel checks.
AI-powered chat simulations for evaluating sales and customer support candidates have been appearing for a while. Solidroad ([related review](/review/uchitsja-ne-dolzhno-byt-skuchno)), which raised €1.1M, and Take2 ([related review](/review/s-tem-zhe-samym-na-drugoj-rynok)), which raised $3.15M, are examples.
But Skillfully's real depth isn't in the simulation technology – it's in the origin story and the structural insight it produced.
The founders graduated from UC Berkeley and found during their own job searches that good roles at good companies were effectively pre-allocated to graduates of more prestigious universities with polished resumes and the right networks. Candidates with genuine ability but the wrong background were filtered out before anyone looked at what they could actually do.
The founding question was simple: what if hiring started with real skills instead of educational prestige? Skillfully is the answer.
Tangent ([related review](/review/sistemnoe-reshenie-bolshoj-problemy)), which raised €1M, takes a different approach to the same problem: it matches candidates from non-traditional backgrounds with mentors who are current employees at target companies, helping those candidates prepare in ways that get them through the door. Currently focused on tech sales roles in the UK.
The broader insight: companies perpetually complain about talent shortages, but much of that shortage is self-inflicted. Resume-based filtering systematically excludes candidates who lack the right pedigree but have the necessary skills or the capacity to develop them quickly. Platforms like Skillfully don't just speed up filtering – they expand the funnel in ways that could meaningfully address the underlying problem.
Skillfully is actively pursuing this expansion. Their TalentNetwork initiative embeds the simulation platform inside university courses and professional training programs as a "Course Companion" – practical assessments that double as a talent pipeline. Student performance data flows into Skillfully's central database, where employers can identify high-potential candidates before they graduate and begin recruiting them for internships, part-time roles, or full-time positions.
Companies pay per simulation use for standard hiring; TalentNetwork access is subscription-based.
The four-stage learning model Skillfully describes for the educational context is worth noting:
1. Identify the student's strengths, potential, and motivators. 2. Develop a universal skill set selected based on those individual parameters – no point drilling an introvert on public speaking. 3. Based on skill acquisition success, identify viable career directions and begin domain-specific training. 4. Based on domain progress, narrow toward specialization.
The underlying principle: teach people to excel at what they're already well-suited for, not to be adequate at an average set of competencies. As Scott Belsky put it: why not design assessments to surface what someone is *exceptional* at in a given domain – math, biology, communication, design – rather than measuring how close they come to a median standard?
That's a potentially transformative approach to professional education. And as Skillfully demonstrates, it can be built into the same platform that solves the hiring problem.
Skills-based hiring – evaluating candidates through job simulations rather than resume screening – is the right direction, and AI has made it substantially more practical to build at scale.
Simulation-based assessment platforms are a timely and promising area. The case for them rests on two related points worth being clear about.
The more important use is *expanding* the hiring funnel, not just filtering it. The opportunity isn't just efficiency – it's accessing candidates who would otherwise be invisible.
The funnel also needs to start somewhere that creates a steady inflow. Skillfully's move to embed simulations inside university courses and training programs is smart: it creates a passive, continuous pipeline of assessed candidates flowing toward employers. That structural advantage is as important as the technology itself.
The educational angle deserves its own consideration: building platforms that identify individual strengths and use them to shape the learning path – rather than measuring everyone against the same standard – could be a distinct and significant direction in professional education.
Skillfully shows these two things can live in one platform. But they're both worth pursuing independently if the combination proves too complex to execute.