Guides and references for structured hiring
Everything MinMaxHR has published on candidate ranking, resume screening software, shortlisting tools, ATS quality, and hiring governance. Short, operational reads: no fluff, no sales pitch. All guides are listed in the sitemap and summarized in llms.txt.
All guides
- The CandidRanker MCP server: run hiring from Claude, ChatGPT or any MCP client: 14 recruiting actions exposed to MCP-capable AI assistants, scoped by OAuth to one workspace. Included on Enterprise.
- MinMaxHR security and data handling: the procurement answers: Data residency in Mumbai, tenant isolation, encryption, malware scanning by default, time-bounded support access, deletion, and the precise SOC 2 position.
- Is AI resume screening legal in India? A DPDP Act guide for HR teams: Yes, with the accountability the DPDP Act expects. What the law actually requires, where screening tools create exposure, and the vendor test to run before buying.
- How CandidRanker scores candidates: the published 8-dimension methodology: The published MinMaxHR methodology: eight weighted dimensions, deterministic scoring, mandatory vs optional skills, why zeros are shown, and how employment gaps are treated.
- What is talent acquisition software?: The category, defined: where ATS, candidate ranking, and Hiring Decision Systems fit - and how to pick a stack that actually works.
- Best ATS for small business: an honest 2026 comparison: Best ATS for small business in 2026: an honest comparison of Workable, Recruitee, JazzHR, BambooHR, and Greenhouse, plus where a Hiring Decision System fits.
- Best resume screening tools in 2026: five approaches, compared honestly: ATS keyword filters, sourcing tools, assessment platforms, black-box AI matchers, and explainable ranking: compared honestly, with where each one wins.
- CandidRanker vs manual resume screening: the honest head-to-head: Manual screening vs explainable AI ranking on speed, consistency, and defensibility, including the cases where manual review is still the right choice.
- Resume screening benchmarks: the numbers behind a defensible shortlist: The reference numbers for screening in 2026: ~250 applications per opening, 7.4-second manual scans, ~100 resumes/minute automated parsing, and 5 minutes to a first ranked shortlist.
- The CandidRanker glossary: every term in AI candidate ranking, explained: Match score, ATS quality score, semantic fit, skill depth, decision states, audit trail, workspaces, API keys, and MCP, defined the way they actually work.
- Who CandidRanker is for: solo recruiters, agencies, and enterprise TA teams: Who it fits and how each team uses it: solo recruiters on the free plan, agencies with per-client workspaces, enterprises with API and MCP integration.
- How CandidRanker works: five minutes to your first ranking: Upload the JD and resumes, review parses, rank across 8 dimensions, record decisions with reasons, export the report: the full workflow, step by step.
- Resume screening software: an honest buyer's guide for 2026: What AI resume screeners actually do, the eight questions to ask before buying, and why explainable ranking beats keyword filters and black-box scores.
- Candidate shortlisting tool: what to look for before you buy: A shortlisting tool should rank applicants against the role, explain every score, and leave the decision with the recruiter. Here is the checklist.
- AI recruitment tools in 2026: what actually helps a hiring team: A practical map of AI recruiting tools (sourcing, screening, ranking, shortlisting, analytics) and the questions that separate useful tools from demos.
- Resume ranking software: rank every resume against the job description: How a fit score works: eight explainable dimensions, deterministic scoring, skill depth and recency weighting, and a ranked list in minutes.
- Bulk resume screening: reviewing hundreds of resumes without burning out: Batch upload, background parsing at roughly 100 resumes a minute, OCR for scanned files, and a ranked list recruiters can act on the same day.
- Recruiting inside an AI assistant: CandidRanker's MCP integration: Connect CandidRanker to Claude via MCP and recruit conversationally: list jobs, rank candidates, and pull shortlists with OAuth-scoped workspace access.
- Improving Quality of Hire through structured candidate evaluation: Quality of Hire is decided before the interview. When evaluation is structured, hiring quality compounds.
- Structured hiring workflows for consistent recruiter evaluation: When evaluation drifts between recruiters, shortlists become unpredictable. Fix the workflow design, not the recruiter.
- Explainable candidate ranking for better shortlists: The strongest candidates surface first - with the reasoning behind every score visible to the recruiter.
- ATS quality scoring and structured hiring readiness: Weak inputs make weak shortlists. Score JD clarity, criteria coverage, and screening consistency before ranking begins.
- Audit-ready hiring workflows and recruiter accountability: Evaluation records, shortlist reasoning, and named accountability - reviewable later, without slowing recruiters down.
- How to measure Quality of Hire, the practical method HR teams actually use: How to measure Quality of Hire: the formula HR teams actually use, the inputs that matter, the 90-day signals, and how to make the KPI defensible to leadership.
- Recruitment automation, done calmly: where to automate and where to stay human: Recruitment automation done calmly: where to automate screening and shortlisting, where to keep recruiters in the loop, and how to measure the lift.
- AI resume screening: what works, what fails, and what to use instead: AI resume screening explained: how it works, where it fails, the bias and compliance risks, and what a recruiter-governed alternative looks like in practice.
- Hiring governance as a property of the workflow, not the policy: Who can see what, who decides, what is recorded - governance lives in workflow design, and that is what makes hiring defensible at scale.
- Human-in-loop hiring workflows for recruiter-owned shortlist decisions: The recruiter stays the decision-maker. No automated rejections - every decision traceable to a named recruiter.
- Explainable candidate evaluation for defensible recruiter shortlists: Recruiters cannot defend shortlists they do not understand. Visible reasoning turns review into a conversation about fit.
- Recruiter-governed hiring workflows for accountable shortlist authority: Recruiters own the criteria, the evaluation, and the shortlist - the platform supports that authority instead of overriding it.
- Policy-aligned hiring workflows for criteria-encoded organisational policy: Policy-aligned hiring encodes organisational hiring policy directly into the criteria recruiters evaluate against, making alignment a workflow property.
- Role-based hiring workflows that scope candidate data to the work: Role-based hiring workflows scope candidate data to the recruiter, hiring manager, or administrator who needs it, supported by tenant isolation.
- Structured candidate evaluation as the foundation of consistent shortlists: Structured candidate evaluation scores every candidate against the same role criteria with the same weights, removing evaluation drift between recruiters.
- Compliant hiring workflows aligned with DPDP, GDPR, and internal policy: Compliant hiring workflows align with DPDP, GDPR, and internal hiring policy by encoding recruiter accountability, access control, and a preserved decision trail.
- Recruitment audit trail as a time-ordered record of every workflow event: A recruitment audit trail preserves criteria, ranking, recruiter reasoning, and shortlist decisions in a reviewable, time-ordered record.
- Auditable hiring workflows that record themselves as recruiters work: Auditable hiring workflows preserve criteria, ranking, recruiter reasoning, and shortlist decisions as a byproduct of the work itself.