1. Define role context
Recruiters provide a role description and review criteria. HanteR structures these requirements so the candidate analysis is tied to the role being reviewed.
AI & responsible use
How HanteR's AI-assisted analysis works
This public technical overview describes the main concepts behind the candidate analysis workflow. It explains what the system can do, what its outputs mean, and where recruiters must apply independent judgment. Exact behavior may vary by enabled features, workflow, and configuration.
From role criteria to reviewer decision
2. Extract candidate information
Supported submitted CV documents are processed into text and normalized candidate information. Extraction quality depends on document formatting and content.
3. Build evidence context
Relevant role requirements and candidate material are assembled into analysis context. Evidence context is used to distinguish documented experience from assumptions or missing information.
4. Generate and check analysis
AI-assisted extraction and narrative generation support skills matching and shortlisting. Structured outputs are checked using validation, grounding, and consistency rules; outputs can be adjusted or flagged when evidence is insufficient.
5. Review and record decisions
Recruiters inspect fit signals, candidate evidence, explanations, and follow-up areas before deciding next steps. The product supports decision records rather than making the final hiring decision.
Interpreting the analysis
A shortlist or match score summarizes how candidate evidence relates to role-specific requirements. Explanations, skill indicators, and suggested follow-up questions provide context for reviewing that result. These outputs are not facts about a person's suitability independent of the underlying evidence.
Evidence checks and oversight
The analysis workflow includes structured parsing and consistency or grounding checks intended to reduce unsupported conclusions. Reviewers remain responsible for examining original context, verifying uncertain claims, and recording any hiring-process decision.
Known limitations and appropriate use
- Missing evidence is not evidence of an absent skill. A CV may omit relevant experience or provide too little detail to assess it.
- Document extraction and language-model outputs can contain mistakes or misinterpret role titles, dates, skills, and experience.
- Similarity scores and shortlist ordering are decision-support indicators, not objective measurements of ability or guarantees of job performance.
- Bias and uneven data quality can affect recommendations. Human reviewers should question results and apply consistent, role-relevant criteria.
- Validation and grounding checks reduce certain errors but do not independently verify every claim or guarantee an error-free result.
This information is not a claim of regulatory certification, guaranteed fairness, or a replacement for an organization's own hiring policies and legal obligations.
Privacy, access, and agent integration boundaries
Candidate documents, private evaluations, and workspace information are not included in this public documentation. Reading these pages does not authorize automated access to protected product features. This page does not advertise a public API or Model Context Protocol (MCP) endpoint.