Vetted White Paper V1.0
Vetted
Making Expert Judgment Accessible, Accountable, and Economically Valuable
White Paper V1.0
Taieb Chaouch, Ameni Bougdima
Legal Disclaimer
This white paper is for informational purposes only and does not constitute an offer or solicitation to sell shares or securities in any jurisdiction. The Vetted protocol and its associated token ($VETD) are not intended to represent any investment contract or guarantee of profit.
Nothing in this document should be interpreted as legal, financial, or investment advice. The development of the Vetted protocol remains subject to change. Participation in the Vetted ecosystem may involve risk, including but not limited to smart contract vulnerabilities, governance shifts, or changes in token economics.
Abstract
Companies make high-stakes hiring decisions based on weak, unaccountable signals: keyword-optimized resumes, reciprocal LinkedIn endorsements, recruiter recommendations incentivized by volume, and referrals with no consequence for failure. None of these signals involve expert evaluation with accountability. The expertise to assess candidates exists but it is simply not accessible, not incentivized, and not structured for deployment.
Vetted solves this by building a credibility infrastructure: a decentralized protocol where domain experts organize into Guilds, define professional standards for their field, review candidates through structured evaluation, and stake tokens and reputation on their judgments. Accurate assessments earn rewards; poor judgments result in slashing. This creates three layers of signal amplification: expert-defined standards that establish what “qualified” means in each domain, stake-backed vetting that filters candidates against those standards, and high-conviction endorsements that signal job-specific fit.
Expert reputation compounds over time. Companies access judgment they could not obtain otherwise. Candidates build verifiable track records that transcend resumes.
We start with hiring because it is concrete, measurable, and universally needed. But the infrastructure extends to any domain requiring an accountable validated expertise.
Note: Vetted provides structured expert judgment to inform hiring decisions. Companies retain full autonomy over who they hire. Experts validate candidates and signal fit; companies make final decisions.
The Problem: Why Hiring Signals Fail
Every organization faces a fundamental coordination problem: how do you identify people you should work with when you lack the expertise to evaluate them directly? Consider hiring a senior designer when your own expertise is in business development. Five candidates apply. Their portfolios look impressive, but are they actually good? Do they understand user research? Can they translate business requirements into interfaces? You need expert judgment you do not possess.
The Fast Path and the Slow Path
Companies face two options, both flawed. The Fast Path relies on speed: screen a resume, check a LinkedIn profile, take a referral, conduct interviews, and hire if the vibes match. It takes weeks but offers no competence guarantee. The Slow Path is the opposite: rigorous assessments, multiple interview rounds, recruiter coordination, and background checks stretching over two months. It drains resources and wears down candidates. Yet even this heavy investment does not eliminate the risk of discovering, three months and thousands of dollars later, that the hire was wrong.
Four Main Broken Signals
Both paths depend on the same four signals, and all four are fundamentally compromised. Resumes have become AI-generated templates optimized for keyword matching, revealing what someone wants you to believe rather than what they can do. Social profiles offer curated performances where endorsements are reciprocal favors and follower counts measure popularity, not capability. Recruiting agencies, while valuable for sourcing, are incentivized to fill positions rather than find fits; they play a numbers game. Referrals should be the gold standard, but in practice they devolve into favor trading and bounty farming where the referrer faces no consequence if their recommendation fails. The common thread: none of these signals involve actual expert evaluation with accountability.
AI Has Made It Worse
The signal problem has been supercharged by AI. Entry-level postings in AI exposed fields have declined over 40% since early 2023 (Revelio Labs). 62% of employers have fired workers over AI inflated resumes that promised skills candidates lacked (LinkedIn/Resume-Now). Postings now attract 200–450+ applications on average, overwhelming pipelines with unverified noise. Hiring has become AI evaluating AI: candidates blast optimized, generated applications; recruiters deploy algorithms to filter them. Authentic talent drowns in the volume.
The Cost of Getting It Wrong
The data confirms this dysfunction. 76% of employers struggle to fill roles with qualified talent (ManpowerGroup 2025). Each bad hire costs $15,000–$40,000 in productivity losses and retraining, rising to $240,000 for executive level mistakes (CareerBuilder 2024). Technical roles take 38–52 days to fill on average (TalentDAO 2025). In Web3 specifically, 40% of hires leave or become inactive within 90 days (Gitcoin Labs), even as Web3 jobs grew 47% year-over-year with critical roles unfilled for months (Coincub 2025). The talent exists. The expertise to evaluate that talent exists. What is missing is the infrastructure to connect them.
The Solution: Credibility Infrastructure
Vetted does not replace your hiring process. It makes it work better. Companies still see resumes, still conduct interviews, still make final decisions. But now they have expert-validated signal that strengthens their judgment and reduces risk. It is a layer of verified expertise added to whatever process you already use.
Think of it as the Michelin star system for professional talent. Michelin stars cannot be bought or gamed. They are awarded by experts with their reputation on the line. They create portable, universally recognized credibility. Vetted credentials work the same way: when domain experts stake their reputation and capital to validate your capability, they create signal that cuts through noise. Unlike unverifiable resume claims or LinkedIn endorsements, Vetted credentials represent accountable expert judgment with economic consequences for inaccuracy.
The protocol achieves this through three core mechanisms:
1. Organized Expertise Through Guilds
Domain experts form Guilds, structured communities that define what “qualified” means in their field. Engineering Guild members collaboratively define what “mid-level React developer” actually means: specific frameworks, architectural patterns, testing practices, performance optimization skills. These are peer-validated professional standards, not generic HR requirements or keyword filters.
2. Accountable Evaluation Through Staking
When experts review candidates or endorse them for specific roles, they stake tokens and reputation. Accurate judgments earn rewards. Poor assessments result in slashing, partial forfeiture of staked tokens and a reduction in reputation score. This creates stake-backed accountability that does not exist in traditional referrals, recruiter recommendations, or hiring agency placements.
3. Continuous Improvement Through Feedback
Every hire generates outcome data. Was the candidate retained? Did they perform well? This feedback flows back to the endorsing experts and to the review panel, refining evaluation criteria and improving the system over time. Expertise does not just exist in this model, it evolves. Experts who consistently identify talent that others overlook build outsized reputation. Guilds refine their standards based on observed outcomes. The protocol compounds its predictive accuracy with every completed hire.
Hiring as Prediction, Not Filtration
Most hiring systems optimize for filtration: remove candidates who do not meet minimum criteria (degree, years of experience, keyword matches). Filtration is computationally cheap but informationally poor. You can filter 1,000 candidates down to 10 without gaining any meaningful signal about which of those 10 will actually perform well. Filtration asks: “Does this candidate pass our threshold?” Prediction asks a fundamentally different question: “Will this candidate succeed in this specific context?” Prediction requires expertise, context, and judgment. It requires someone who understands both the role requirements and the candidate’s capabilities well enough to forecast fit. This is what Vetted provides.
Three Layers of Signal Amplification
Vetted does not merely filter candidates. It amplifies signal through three complementary layers, each adding a different dimension of predictive value.
Layer 1: Expert Defined Standards
Guild members, experienced practitioners in each domain collaboratively define what competence looks like at each level. These are not HR-written requirements or keyword templates. They are peer-validated professional standards created by people who do the work. This establishes a domain-specific baseline against which all candidates are evaluated.
Layer 2: Stake-Backed Vetting
A randomly selected panel of 5–7 Guild experts reviews each candidate proposal using standardized rubrics covering skill match, relevant experience, motivation, and credibility. Reviewers stake tokens to participate. Scores are aggregated using a robust statistical method that neutralizes outliers. Reviewers aligned with the collective consensus earn rewards; those significantly misaligned face slashing. Vetting establishes baseline qualification “is this person capable?” rather than job-specific fit.
To protect against false negatives, Vetted provides a decentralized arbitration path. If an expert believes a qualified candidate was incorrectly rejected, they can stake tokens to flag the decision for appeal. Senior Guild reviewers re-evaluate the proposal. If overturned, the candidate is admitted and the appealing expert earns rewards. This ensures exceptional candidates with unconventional backgrounds are not filtered out by consensus bias.
Layer 3: Job-Specific Endorsement
A candidate might be generally qualified but wrong for a specific role. When a Guild-approved candidate applies for a job, experts who believe there is a strong match can endorse them by staking tokens in a sealed-bid process. Only the top three endorsers are selected. This is high-conviction signaling: experts are saying “I am confident enough in this match to put my capital and reputation on the line.” Endorsers are rewarded for successful hires and slashed for poor matches. The incentive is accuracy, not conformity. An expert who consistently spots exceptional talent that others overlook builds outsized reputation through successful endorsements.
The Compounding Effect
Each layer actively strengthens the signal from the previous one, transforming hiring from filtration (removing bad candidates) to prediction (identifying successful matches). An endorsed candidate arrives in a hiring company’s queue with three verified layers: Guild membership confirming baseline qualification, an AI-assisted match score assessing role-specific fit, and expert endorsements backed by economic stakes.
What This Looks Like in Practice
Leila’s Journey
Meet Leila, a mid-level frontend developer with React and Web3 experience, currently freelancing and looking for a stable remote role.
Joining and submitting a Guild application. Leila signs up on Vetted, creates her profile with her resume, GitHub, and LinkedIn. She fills an application to join the Engineering Guild at the Intermediate level, including her work portfolio, contributions to a DAO frontend project, and a short motivation letter.
Expert review. A randomly selected group of experienced Engineering Guild members reviews her application anonymously. They rate her on skills, experience, motivation, and credibility. Her application is accepted.
Job matching and endorsement. Now officially an Engineering Guild member, Leila can view relevant frontend developer job listings. For each listing, she sees a matching score. She applies to a role with a 90% match score. Her past work catches the attention of two experts, who endorse her by staking tokens, boosting her visibility.
Outcome. A hiring company interviews her and makes an offer. The experts who endorsed her receive a portion of the platform’s success fee as a reward. Leila starts her new role without ever submitting her resume to a black hole.
The Feedback Flywheel
Every mechanism in the protocol reinforces the others: candidate submits proposal → experts review → if accepted, AI generates match scores → candidate applies to jobs → experts endorse → if hired, experts earn rewards and AI improves → expert reputation increases → higher reputation yields higher rewards and governance influence → repeat.
Each cycle incentivizes accuracy, discourages abuse, learns from outcomes, and strengthens over time.
How Vetted Works: Architecture Overview
Vetted is built on a four-stage vetting pipeline supported by economic incentives, AI assistance, and decentralized governance.
Guilds: Organized Expertise
Guilds are domain-specific communities of vetted experts who collectively set standards, review candidates, and govern their field’s hiring practices. Each Guild operates semi-autonomously with tiered membership (Recruit → Apprentice → Craftsman → Officer → Guild Master), meritocratic advancement based on contribution quality, internal governance for standard-setting, reputation systems that track evaluation accuracy, and collaborative spaces for knowledge sharing and mentorship.
Guilds at launch span eight professional domains: Engineering, Product, Design, Marketing & Growth, Operations & Strategy, Finance, Legal & Compliance, Sales & Success, and People, HR & Recruitment. Each Guild defines what “qualified” means in their field. New Guilds and sub-guilds are created through protocol governance as demand warrants.
The Vetting Pipeline
Stage 1: Candidate Guild Application Submission
Candidates apply to join a Guild by submitting a structured application: resume, portfolio, relevant experience, social profiles, and motivation statement. Vetted maintains uniform vetting for all candidates regardless of external credentials. Guild membership is earned through expert peer review of actual work and capability, not proxies like social media presence or institutional affiliation. A well-prepared application takes approximately 30–45 minutes, an intentional quality filter that discourages low-effort applicants.
Stage 2: Expert Review Panel
A randomly selected panel of 5–7 Guild experts reviews the application using standardized rubrics. Reviewers stake tokens to participate, scores are submitted privately, and the panel’s collective assessment is aggregated to produce a final candidate score. If accepted, the candidate joins the Guild with a summary of reviewer-identified strengths. If rejected, they receive structured feedback with specific improvement guidance. Target turnaround is 48–72 hours.
Stage 3: AI Assisted Matching
Once admitted, candidates can apply to jobs posted within their Guild. For each role, an AI matching engine compares the candidate’s verified application data against specific job requirements, generating a match score visible to candidates, companies, and experts. The AI operates only on data validated by the review panel. It cannot surface a candidate who has not passed vetting and cannot override expert endorsements.
Stage 4: Expert Endorsement
When a Guild-approved candidate applies for a role, experts can endorse them through a sealed-bid auction competing for three endorsement slots per candidate. Bids are private during the window; the top three stakes are selected when it closes. If the candidate is hired and retained, endorsers earn a share of the platform’s success fee. If the candidate is rejected or performs poorly, endorsers face proportional slashing. For full mechanism specifications, including staking amounts, lock periods, slashing severity scales, and dispute resolution processes, see the Technical Appendix.
Stage
Conducted By
Question Answered
Outcome
1. Proposal
Candidate
Has this person invested effort in presenting capability?
Application enters review queue
2. Expert Review
5–7 Guild experts (random)
Is this person genuinely qualified?
Accepted or rejected with feedback
3. AI Matching
Platform matching engine
Which roles best fit this candidate?
Per-job match score
4. Endorsement
Individual Guild experts
Will this candidate succeed in this specific role?
High-conviction signal to hiring company
Incentive Design
Vetted’s incentive architecture is rooted in game theory. In traditional hiring, incentives are misaligned: recruiters are paid to fill seats, referrers face no consequences for bad recommendations, and candidates have every reason to embellish credentials. Vetted restructures these dynamics through stake-backed accountability, transforming hiring from a one-shot transaction with asymmetric information into a repeated game where honest, accurate judgment is the dominant strategy.
For Experts
Experts earn tokens for aligned reviews and successful endorsements, building reputation that increases future earning potential and governance influence within their Guild. They face slashing for skill mismatch predictions, but critically, not for uncontrollable factors like company culture or management failures. Beyond token earnings, experts gain access to exclusive networks of domain peers and decision-makers, real-time market intelligence on skills demand and salary trends, and the ability to shape professional standards as Guild Officers and Masters. Reputation compounds across the ecosystem, establishing portable, on-chain professional authority.
For Candidates
Candidates build verifiable track records through Guild acceptance and endorsements, receive structured feedback for improvement if rejected, and access jobs with pre-qualified expert support. Guild membership itself provides value: access to a domain expert network, professional development resources curated by practitioners, and market intelligence on hiring trends.
For Companies
Companies access pre-vetted talent pools with expert validation while retaining full hiring autonomy. They receive high-signal endorsements from accountable experts, reduce time-to-hire and cost-per-hire, and contribute outcome feedback that improves the system. Vetted does not make hiring decisions on a company’s behalf; it produces a layered, expert-validated signal. The final decision remains entirely with the company.
These incentives create a self-correcting system: good actors are rewarded, bad actors are filtered out, and the quality of evaluation continuously improves. For the complete game-theoretic analysis, staking mechanics, slashing severity scales, and reward distribution formulas, see the Technical Appendix.
The $VETD Token
$VETD is the native utility token of the Vetted protocol. It is not a speculative instrument; it is the economic substrate that makes accountable expert judgment possible at scale. Every core action on Vetted requires $VETD, and every outcome feeds back into rewards or slashing. Demand is generated by platform activity, not external market dynamics.
The token serves six functions within the protocol:
Function
Mechanism
Accountability
Vetting Access
Stake tokens to enter reviewer pool
Misaligned vote → reputation loss + slashing
Endorsement Bidding
Sealed-bid auction; top 3 win slots
Failed hire → proportional slashing
Governance Voting
Vote on protocol rules, fees, upgrades
Merit-weighted democracy model
Proposal Submission
Stake to submit governance proposals
Stake forfeited if classified as spam
Appeal Staking
Stake to trigger arbitration on rejected candidate
Failed appeal → full stake forfeited + reputation penalty
Reward Distribution
Fixed pool distributed by reputation tier
Reward share scales: ×1.00 / ×1.25 / ×1.50
Rewards and penalties scale with reputation. High-reputation experts earn more when right but lose more when wrong. Newcomers are protected from severe slashing while building track records. Reward vesting, inactivity decay, and reputation-weighted distribution structurally favor experts who participate consistently over those who engage opportunistically. Full staking mechanics, lock periods, and slashing schedules are detailed in the Technical Appendix.
Privacy and Transparency
Web3 is built on radical transparency: smart contracts are public, transaction histories are immutable, and decision-making is auditable. But hiring requires privacy. Job applications, performance evaluations, and peer reviews are inherently sensitive. Publishing them openly creates reputational risk, enables discrimination, and violates professional confidentiality. This creates a design constraint: how do you build a system that is transparent in process while remaining private in identity?
Vetted solves this through architectural separation, making the mechanics auditable while keeping the humans confidential.
What Is Transparent
Process integrity is fully on-chain and verifiable. Proposal scores are recorded as cryptographic hashes, proving what was evaluated without revealing content. Reviewer votes are publicly auditable after resolution, showing consensus patterns. Endorsement stakes are visible, creating accountability. Success fees and slashing events are recorded, tracking expert accuracy over time. Anyone can verify that the system operates as designed. No central authority can manipulate outcomes invisibly.
What Is Private
Personal information remains confidential throughout. User identities are pseudonymous, experts operate under wallet addresses, not legal names (Although an Expert can decide to disclose their real name). Review panels are randomly selected and gain only temporary access to applications during their review window. Reviewer identities and votes are hidden until finalized, preventing coordination or bias. Job applications are anonymized during endorsement: experts see match scores, job-relevant skills, and previous experiences but not names, photos, or demographic markers. This shifts the focus from “who you are” to “what you can do.”
The system uses a hybrid architecture: on-chain records for votes, stakes, slashing events, reputation scores, and outcomes; off-chain encrypted storage for full application content, personal identifiers, and detailed evaluations; and cryptographic anchoring where application hashes stored on-chain prove that what was reviewed matches what was submitted, without exposing content to the public blockchain. Trust does not require exposing everything. It requires proving what matters.
Governance: Merit Weighted Democracy
Traditional token-weighted voting (1 token = 1 vote) creates plutocracy. Pure democratic voting (1 person = 1 vote) ignores domain knowledge and economic stake. Vetted uses a hybrid model that balances expertise recognition, economic alignment, and egalitarian access.
Core Formula
Vote Weight = 1 × (1 + Reputation Multiplier), where the base vote is 1 for every verified participant and the Reputation Multiplier is capped at 2.0 (calculated as Reputation Score ÷ 1,000, maximum 2.0). This yields a vote range of 1.0 to 3.0 per person. A whale with 100,000 $VETD but zero reputation gets 1 vote. An expert with 200 tokens and 2,000 reputation gets 3 votes. Expertise beats capital.
Guild Masters, elected leaders who represent their Guild in protocol governance receive an additional 1.5× role multiplier, yielding a maximum of 4.5 votes. This role is term-limited (6–12 months) and revocable by Guild vote, preventing permanent power accumulation.
Participation Requirements
Governance eligibility requires three criteria: Decentralized Identity (DID) verification to prevent Sybil attacks while preserving privacy through zero-knowledge proofs; a minimum $VETD token stake ensuring economic alignment; and active participation within the last 90 days through reviewing proposals, endorsing candidates, or voting in governance. This prevents dormant accounts from suddenly influencing decisions without context or commitment.
Two-Tier Structure
Vetted implements federated governance across two layers. Guild-level governance grants each domain-specific Guild autonomous control over admission standards, evaluation rubrics, reputation thresholds, mentorship structures, and internal dispute resolution. Protocol-level governance addresses cross-Guild decisions: smart contract upgrades, tokenomics parameters, treasury allocation, Guild creation or retirement, and cross-Guild policies.
Progressive Decentralization
Governance follows a four-phase path from centralized to fully community-driven, with objective on-chain criteria gating each transition. Phase 1 (Foundation, months 0–12): core team governance with community advisory, focused on proving product-market fit. Phase 2 (Guild Autonomy, months 12–24): Guilds govern internal operations; core team retains veto on platform decisions. Phase 3 (Shared Governance, months 24–36): full merit-weighted voting activated; core team participates as reputation-weighted voters with emergency multisig only. Phase 4 (Full Decentralization, month 36+): all decisions through on-chain voting; an elected Technical Council replaces Founder approval for major changes. Each transition requires specific milestones, minimum active Guilds, completed hires with retention data, governance participation rates; not just elapsed time. The full transition criteria and attack resistance mechanisms are detailed in the Technical Appendix.
Why Web3 Infrastructure
Vetted uses blockchain because decentralized infrastructure is the only way to deliver these properties simultaneously at scale.
Transparent verification without central control. All reviews, endorsements, and outcomes are recorded on-chain for auditable history. No trusted central authority is needed, and no single party can manipulate outcomes.
Economic accountability via cryptographic mechanisms. Staking and slashing impose real consequences for poor judgment; impossible in traditional advisory models where experts face zero downside risk.
Permissionless participation with merit-based gatekeeping. Anyone can join as an expert or candidate. Entry is gated by peer review and proven performance, not institutions or credentials.
Portable, composable reputation. On-chain reputations are verifiable assets across protocols and contexts. No platform silos.
Centralized platforms cannot deliver these properties: closed systems enable outcome manipulation, scores remain trapped inside platforms, participation is gatekept by the platform operator, and data is non-composable. This architecture renders fraudulent validation economically irrational. Experts stake tokens and reputation on judgments. Outcomes are tracked, with failed endorsements triggering slashing. Standards are practitioner-defined, not algorithmic. Consensus prevents bias; arbitration protects valid outliers. Reputation compounds, rewarding consistent accuracy.
As AI floods markets with synthetic signals and credentials lose meaning, verifiable human judgment with economic stakes becomes scarce and essential. Vetted is that infrastructure. Companies and candidates trust each other’s expertise, secured by trustless infrastructure. These properties are not optional features; they are the architectural foundation for organized expertise in a noisy era.
Dimension
Traditional Market
Web3 Reputation
Vetted Protocol
Signal Mechanism
Resumes, referrals, recruiter judgment
Identity verification, credential aggregation
Collective expert evaluation with staking
Accountability
None; no consequence for poor recommendations
None; advisory with no stakes on accuracy
Economic: slashing for inaccurate judgments
Outcome Feedback
No closed loop; hiring and outcomes disconnected
No hire-level tracking
Full loop: hire outcomes refine expert reputation
Participant Incentives
Asymmetric; recruiters paid per fill, not fit
Token incentives, but not tied to domain accuracy
Aligned via tokens + reputation, tied to outcomes
Scalability
Linear per hire
Scales identity, not evaluation
Network effects: more experts improve signal density
Job boards like LinkedIn and Indeed aggregate attention, not judgment. Centralized expert networks like GLG offer paid consultations with no stakes on accuracy. Web3 reputation systems like Gitcoin Passport verify identity but do not validate domain-specific capability or link to hiring outcomes. Emerging decentralized hiring platforms experiment with credentials and matching but lack stake-backed accountability. Vetted creates a new category: it matches trust to judgment, makes expert evaluation accessible and accountable, and lets the market emerge from there.
Future Applications
The infrastructure Vetted builds, Guild-organized expertise, stake-backed evaluation, outcome-based reputation, generalizes beyond hiring to any domain where expert judgment meets uncertainty and outcomes are measurable. Planned extensions include an Expert Opinion Marketplace, where companies and DAOs create decision markets within specific Guilds for high-stakes strategic questions, and Curated Learning Paths, where practitioners stake reputation to validate educational resources, creating living curricula maintained by experts with skin in the game. Additional applications span grant evaluation, DAO governance advisory and hyper-specific talent discovery. These extensions will be detailed in dedicated publications as the core hiring infrastructure proves its value. Hiring is where Vetted proves the primitives work. The pattern generalizes.
Conclusion
Hiring fails when expertise is not accessible, not accountable, and not economically valuable. Vetted solves this by transforming expert judgment from an ad-hoc resource into systematic infrastructure. Through Guild-organized standards, stake-backed reviews, and high-conviction endorsements, the protocol creates three layers of signal amplification that turn hiring from filtration into prediction.
Vetted is not anti-AI. We use AI throughout the platform for matching candidates to roles, generating feedback, and surfacing insights from data. What we oppose is AI without accountability: synthetic credentials that cannot be verified, automated evaluations with no consequences for error, and systems where machines judge machines while humans are left out of the loop. Vetted puts humans back at the center. AI assists; experts decide. Algorithms surface candidates; practitioners validate them.
In a world where Vetted succeeds, talented people are no longer invisible, a skilled developer without a CS degree proves capability through expert validation, not self-promotion. Companies make better hiring decisions faster, replacing the $40,000 cost of a bad hire with expert-validated signal. Senior professionals are not giving away judgment informally; they are building on-chain authority, earning rewards for accuracy, and shaping standards in their field. Human validation becomes the scarce, valuable signal in an era of AI-generated noise. And the peer review mechanics that worked in small communities, craftsmen vouching for craftsmen work at internet scale, with accountability mechanisms that prevent gaming.
This is infrastructure for organized expertise: where human judgment becomes deployable at scale, accountable through incentives, and continuously improving through outcomes. We believe the future of work requires systems that are intelligent and accountable, automated and human-centered, fast and trustworthy. Vetted is building that future.
References
Woolley, Anita et al. “Evidence for a Collective Intelligence Factor in the Performance of Human Groups.” Science, 2010.
Engel, David et al. “Reading the Mind in the Eyes or Reading Between the Lines?” PLOS ONE, 2014.
ManpowerGroup. “Talent Shortage Survey.” 2025.
SHRM. “State of the Workplace Report.” 2025.
Coincub. “Web3 Jobs Report.” 2025.
CareerBuilder. “Annual Survey: Cost of Bad Hires and Recruiting Trends.” 2024.
TalentDAO. “Web3 Hiring Survey.” 2025.
Gitcoin Labs. “DAO Contributor Lifecycle Study.” 2024.
Revelio Labs. “AI and Entry-Level Employment Trends.” 2024.
Persol APAC. “Cost of a Bad Hire Report.” 2025.
Technical Appendix:
Contact
taieb@vettedprotocol.com
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