65% of hiring managers say AI resumes are making hiring harder. Applications are up 239%. And 91% of recruiters have caught candidates faking credentials. The tools were supposed to help. They made it worse.
DevCard is structured evidence of what candidates actually did — transparent, traceable, and built to recover the signal you've lost.
One job. Three candidates. Can you pick the best fit?
What they need:
Team context:
Full-stack backend at large tech company. Microservices, cloud infra, strong keyword match.
Interview focus:
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Enterprise backend at insurance company. Different stack, no obvious keyword match.
Interview focus:
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Mid-level backend at SaaS startup. Solid Python, some Go. Consistent shipper.
Interview focus:
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Click candidates to rank them #1, #2, #3. Click again to remove.
You matched DevCard's ranking.
You looked past the keywords. Most people don't. Priya's real-time financial processing at $200M/month and active fintech motivation outweigh Marcus's surface-level stack match.
Your ranking differs from DevCard's.
Marcus has every keyword — but hasn't shipped under production constraints in 3 years. Priya's real-time financial processing at $200M/month transfers directly to payment infrastructure, and she's actively seeking exactly this kind of role. Jordan's consistent delivery and SaaS experience put him ahead of Marcus's stalling trajectory.
What if NovaPay needed someone immediately?
Switch the hiring stance to see how priorities shift the rankings.
Growth Oriented weights transferability and delivery strength equally — surfacing candidates who can ramp and own outcomes long-term.
Immediate Impact puts 45% weight on Direct Readiness. Marcus's stack match matters more — but his delivery and motivation gaps still drag the composite down.
A thousand applicants for one role and they all read like the same person. Same power verbs, same "grew revenue 25%," same stack list. Hiring managers are spending more time than ever and trusting the process less.
Recruiters are handling 93% more applications than in 2021. Only 21% are confident they're not rejecting good people. And 46% of candidates say their trust in hiring has declined — with 42% blaming AI directly.
The resume is cooked. Not because it was a bad idea — but because the signal it carried has been completely destroyed by noise.
Faster screening. Smarter filters. AI scoring candidates at scale behind closed doors.
The result? Secret scores. Vendors generating hidden employment reports without candidates knowing they exist. Black-box systems ranking people before a human sees their name. Candidates have no idea why they were filtered out — or that they were.
70% of hiring managers say AI helps them decide faster. Only 8% of job seekers call it fair. That gap isn't a PR problem. It's a trust crisis.
More AI on a broken process doesn't fix the process. It makes the brokenness faster.
Instead of screening faster, we make the evidence worth reading.
Developers on DevCard build a structured career record — not a narrative, a record. Real projects, real tasks, real responsibility. What they shipped, what layer they worked at, what constraints they operated under, who verified it. The system rewards specificity. Vague claims produce weak profiles.
When you post a role, DevCard scores every candidate across four dimensions. Not a single mystery number. Four distinct answers to four distinct questions — each backed by traceable evidence you can inspect.
Every score links back to specific projects, tasks, and peer verification. You can always ask "why did this person score a 74 here?" and get a real answer.
Can this person do this specific job with minimal ramp? How much of what you need have they already practiced, in production, recently? This is the "day one impact" dimension.
What underlying capability transfers even when the exact tools don't match? A payments engineer might score 38 on Direct Readiness for an infrastructure role but 71 on Transferable Capability — because the systems thinking, the failure handling, and the production discipline all carry over. This is the "adjacent fit" dimension. The person you would have missed.
Can they finish things? Ship to production under real constraints? Own the outcome when it gets ambiguous? This separates the person who can start a project from the person who can land it safely. Completion over output.
Does the candidate actually want this kind of work? Are they moving toward it or away from it? Will the environment sustain them? Ability without motivation is a bad hire waiting to happen. This dimension is based entirely on what candidates tell us about their direction — nothing inferred, nothing scraped.
Each dimension carries its own confidence score — how much evidence backs this particular assessment. High ability with low confidence means "promising but verify." You always know where the uncertainty is.
Most tools optimize for filter-out. DevCard is built for filter-in.
The transferability engine surfaces candidates whose underlying capability matches your requirements even when the surface-level keywords don't. An embedded systems engineer who scores well for your backend role. A game developer whose real-time systems experience maps to your infrastructure needs.
70% of employers now use skills-based hiring. But most tools still match on keywords. DevCard actually delivers what skills-based hiring promises — evidence of what a person can do, not what words appear on their resume.
Keyword matching killed creative hiring. DevCard is designed to restore it.
Every score is traceable. Every number links to specific evidence — projects, tasks, peer verification. If a hiring team sees a candidate's scorecard, the candidate sees it too. Same data, same view, full transparency.
Developers choose to be here. Every profile is built by the developer themselves. Opt-in evidence is fundamentally different from a scraped database — the data is trustworthy because people built it deliberately.
Motivation comes from the candidate. Career direction is based on what developers explicitly tell us — what they want, what they're moving toward, what environments sustain them. Nothing inferred from gaps, geography, or browsing behavior.
Developers control their data. When developer interests and company interests conflict, developers win. That's the architectural constraint the system is built on — and it's why the data is worth trusting.
Scoring is deterministic. AI extracts and structures evidence from career histories. The scoring itself is fully deterministic — transparent math, traceable evidence, human decisions. You can always ask "why?" and get a real answer.
Not every company hires the same way. DevCard doesn't pretend there's one universal hiring logic. When you post a role, you choose a stance:
Immediate Impact
Prioritizes Direct Readiness. You need someone who can contribute from week one. The scoring weights shift to favor exact-match experience and current practice.
Growth Oriented
Weights Transferable Capability and Delivery Strength more heavily. You're willing to invest in ramp because you're hiring for long-term fit. This is where the unexpected hires surface.
Motivation Priority
Elevates Motivation Alignment. You've been burned by great-on-paper hires who left in eight months. You want someone who actually wants this work, in this environment, for the right reasons.
Same candidates, ranked differently, because you told the system what you actually care about.
LinkedIn endorsements are noise. Generic recommendations are noise. "Great team player" tells you nothing.
DevCard verification is specific and scoped. Colleagues confirm bounded factual claims:
"Owned failure handling and rollout strategy for the payments migration."
"Led backend architecture decisions for the internal tooling system."
"Responsible for the search indexing pipeline — designed it, shipped it, ran it in production."
Each verification strengthens the confidence score for affected dimensions. Candidates control who gets asked and when. No one is contacted without their explicit permission.
In a market where 91% of recruiters have caught candidate deception, scoped peer verification is the difference between trusting a claim and knowing it's real.
It worked when there were 50 applicants, not 500. When people wrote them by hand, not generated them by prompt. What replaces it has to be built on evidence, not narrative. On transparency, not black boxes. On trust that compounds — not profiles that perform.
Free during early access. No credit card required.