Hire engineerswith evidence, not guesswork
Elanqo AI is being built to organize technical signals from resumes and candidate-provided projects, surface decision-support context, and give candidates role-specific improvement paths.
Private beta
Elanqo is currently in private beta. The public product is still under active development.
Evaluation Preview
ExamplePossible improvement area
Fix date consistency
Example recommendation
Content gap
Missing summary quality
Suggested edit available
Pipeline Transparency
How evidence-basedhiring works
The workflow is designed to make each stage easier to review, from extraction to score delivery.
Pipeline Visualization
Stage 1 / 4CV Upload
Submit resume
Skill Extraction
AI analysis
Verification
Link validation
Analysis
Evidence scoring
Report
Final decision
Evidence Collection
The parser is designed to identify role signals, quantified bullets, and timeline metadata so reviewers can see a clearer candidate profile.
CVs, evolvedby AI
Create, refine, and manage professional CV drafts. AI-assisted suggestions are being developed to help tailor versions for different roles.
Alex Chen
Senior Software Engineer
Sarah Mitchell
Product Manager
Jordan Lee
UX/UI Designer
AI-Powered Generation
Generate polished CVs from scratch, or let AI enhance your existing content with role-specific improvements.
Multiple Templates
Choose from professionally designed templates for tech, business, design, and new graduates.
Versioned and Reusable
Clone and tailor CV versions for different roles without starting over.
MVP Access
The CV workflow is planned for private beta access while the product is still being developed.
CV data handling is described in our privacy notice. Every CV should remain yours to keep, export, and share.
Evidence thatspeaks for itself
Move beyond resume claims by connecting skills to candidate-provided proof. Assessments are designed to show supporting evidence and transparent reasoning.
Code Evidence Review
Candidate-provided repositories can be reviewed for relevant signals such as complexity, consistency, and skill demonstration
Portfolio Review
Candidate-provided project links can add context about functionality, design choices, and claimed technologies
Experience Context
Work history can be reviewed alongside candidate-provided context to reduce unsupported resume claims
Assessment Support
Technical skills can be explored through contextual questions and practical assessment workflows
Evidence Example
See how skills can be presented with supporting sources and confidence indicators
Compact View
Flowing Layout
Python
React
TypeScript
Node.js
Docker
Detailed Evidence
Python
E3Evidence
Machine Learning
E2Evidence
For Recruiters
Built for modernHR teams
Organize review context, reduce repetitive screening work, and keep hiring decisions explainable from first screen to final shortlist.
Clearer Hiring Context
Evidence-driven scorecards help reviewers compare role-relevant signals with more structure.
Streamlined Process
Evaluation workflows are designed to reduce repetitive screening steps and support faster review.
Traceability and Review
Transparent rationale and audit trails can support human-led hiring review.
Candidate Experience
Clear feedback loops can help candidates understand role-fit gaps and next steps.
Built for private beta feedback
Public customer results will be added after real deployments and permission.
Customer stories and measured outcomes will be published after real beta usage and permission.
The product is designed as decision support. Hiring teams remain responsible for final decisions.
Candidate-provided sources, score explanations, and reviewer notes are kept visible together.