Full Protocol
Due Diligence
End-to-end analysis across 6 dimensions: website, whitepaper, tokenomics, team, on-chain activity, and GitHub codebase. Dual-layer AI analysis with human expert sign-off.
6
Analysis Dimensions
3–7d
Avg Delivery Time
50M+
Papers in Plagiarism DB
0–100
Composite Risk Score
What We Analyze
Six structured analysis dimensions, each producing scored findings with citations and remediation recommendations.
Website & Social Presence
- check_smallSSL certificate validity and configuration
- check_smallSecurity headers (CSP, HSTS, X-Frame-Options)
- check_smallDomain registration history and WHOIS analysis
- check_smallSocial media account age and engagement authenticity
- check_smallPhishing domain monitoring
Whitepaper Technical Analysis
- check_smallTechnical feasibility of proposed architecture
- check_smallPlagiarism detection against 50M+ academic papers
- check_smallConsistency between whitepaper and smart contract code
- check_smallRoadmap credibility and timeline assessment
- check_smallEconomic model validity and assumptions review
Tokenomics & Distribution
- check_smallToken allocation fairness (team, investors, community)
- check_smallVesting schedule adequacy and cliff analysis
- check_smallInflation/deflation model sustainability
- check_smallLiquidity pool structure and lock verification
- check_smallOn-chain distribution vs. stated allocations
Team Background
- check_smallLinkedIn profile verification and employment history
- check_smallGitHub contribution history and code authenticity
- check_smallPrevious project track record and exit analysis
- check_smallOn-chain wallet history and past protocol involvement
- check_smallCross-reference with KYC if applicable
On-Chain Activity
- check_smallSmart contract deployment history
- check_smallTreasury wallet activity and spending patterns
- check_smallToken holder concentration analysis (Gini coefficient)
- check_smallWash trading and manipulation indicators
- check_smallBridge interactions and cross-chain exposure
GitHub Codebase Review
- check_smallCommit history authenticity and contributor consistency
- check_smallTest coverage percentage
- check_smallDependency audit (npm audit / cargo audit)
- check_smallSecret scanning (hardcoded keys, API tokens)
- check_smallCode-to-whitepaper consistency check
DeepSeek Dual-Layer Analysis
Our adversarial AI architecture reduces hallucinations and false positives by having two independent models challenge each other's output.
Data Collection
Automated scrapers gather all public data: website, social, GitHub, on-chain, legal registries.
AI Layer A — DeepSeek Primary
Primary model processes all collected data, generates structured findings with citations and risk flags.
AI Layer B — Adversarial
Second model challenges Layer A — verifies claims, identifies gaps, reduces false positives.
Human Analyst Review
Domain expert reviews AI output, adds contextual judgment, assigns final risk score.
Report & Badge
Structured PDF report + public audit page with 0–100 risk score and actionable recommendations.
What You Receive
PDF Report
Full audit report with all findings, scores, and recommendations
Public Audit Page
Permanent URL with your project's audit badge and summary
Risk Score 0–100
Composite risk score across all 6 dimensions with breakdown
Action Plan
Prioritized remediation roadmap with effort estimates