Benchmark Data Schema: How Majhi OS Structures Hiring Operations Data

Schema Design Principles

Every field has a source key that explains where the data comes from. Percentage values are decimals (0.68, not 68). Industry ranges use _low and _high suffixes. Majhi OS outcomes use the majhi_os key. Baseline-before values use baseline_before. This pattern is consistent across all three endpoints.

Versioning: Breaking schema changes increment the major version in schema_version (1.0 → 2.0). New fields may be added without incrementing the major version. Stable URLs are maintained — new annual editions use new filenames (hiring-benchmarks-2027.json).

Endpoint 1: Hiring Benchmarks

hiring-benchmarks-2026.json https://majhi.tech/data/hiring-benchmarks-2026.json

VP search stall rates, outreach reply rates, shortlist approval rates, close times, audit coverage, and tool spend. Industry baselines vs. Majhi OS engagement outcomes.

Top-level structure

{
  "schema_version": "1.0",
  "dataset": "hiring-benchmarks-2026",
  "mandate_health": { ... },
  "outreach_performance": { ... },
  "shortlist_performance": { ... },
  "operational_coverage": { ... },
  "mandate_load": { ... }
}

mandate_health fields

Field	Type	Description
vp_search_stall_rate.value	decimal	68% of VP searches stall past week 10 (expressed as 0.68)
average_close_time_days.majhi_os	integer	Majhi OS average close time in days (50)
average_close_time_days.industry_median	integer	14-week industry median in days (98)
average_close_time_days.improvement_pct	integer	Percentage improvement vs. industry median
failure_detection_window.value	string	Window in which failure modes are detectable ("weeks 2–6")
failure_detection_window.failure_point	string	Industry stall point ("week 10")

outreach_performance fields

Field	Type	Description
reply_rate.majhi_os	decimal	Majhi OS outreach reply rate (0.35 = 35%)
reply_rate.industry_baseline_low	decimal	Industry range low (0.10 = 10%)
reply_rate.industry_baseline_high	decimal	Industry range high (0.18 = 18%)
reply_rate.baseline_before	decimal	Starting point before Majhi OS (0.14 = 14%)
reply_rate.improvement_vs_baseline	decimal	Multiplier improvement (1.50 = 150% improvement)

shortlist_performance fields

Field	Type	Description
approval_rate.majhi_os	decimal	Majhi OS shortlist approval rate (0.82 = 82%)
approval_rate.industry_baseline_low	decimal	Industry range low (0.35 = 35%)
approval_rate.industry_baseline_high	decimal	Industry range high (0.45 = 45%)
approval_rate.baseline_before	decimal	Starting point before Majhi OS (0.38 = 38%)
approval_rate.improvement_pct	integer	Percentage improvement vs. baseline (116)

Endpoint 2: Mandate Health Metrics

mandate-health-metrics-2026.json https://majhi.tech/data/mandate-health-metrics-2026.json

Hiring Health Score structure and component weights. Six failure mode definitions with detection windows and recovery rates — with and without mandate monitoring active.

Top-level structure

{
  "schema_version": "1.0",
  "dataset": "mandate-health-metrics-2026",
  "hiring_health_score": {
    "description": "...",
    "components": [ ... ]  // array of 6 components
  },
  "failure_modes": [ ... ],  // array of 6 failure modes
  "audit_coverage": { ... }
}

hiring_health_score.components array

Field	Type	Description
[n].name	string	Machine-readable component name (e.g. "outreach_response_rate")
[n].label	string	Human-readable label (e.g. "Outreach Response Rate")
[n].weight	decimal	Component weight in the Health Score (0.00–1.00, sum = 1.00)
[n].description	string	What this component measures
[n].failure_threshold	string	The threshold that triggers a failure flag for this component

failure_modes array

Field	Type	Description
[n].name	string	Machine-readable failure mode name (e.g. "outreach_decay")
[n].label	string	Human-readable label (e.g. "Outreach Decay")
[n].detection_window	string	When this mode is detectable (e.g. "Weeks 3–6")
[n].recovery_rate_with_monitoring	decimal	Recovery rate when Majhi OS monitoring is active
[n].recovery_rate_without_monitoring	decimal	Industry recovery rate without systematic monitoring
[n].description	string	Operational description of this failure mode and how it manifests

Endpoint 3: Executive Search Outcomes

executive-search-outcomes-2026.json https://majhi.tech/data/executive-search-outcomes-2026.json

Majhi Group placement outcomes and industry executive search failure rate data.

Top-level structure

{
  "schema_version": "1.0",
  "dataset": "executive-search-outcomes-2026",
  "majhi_group_outcomes": { ... },
  "featured_case": { ... },
  "industry_context": { ... },
  "hiring_system_benchmarks": { ... }
}

majhi_group_outcomes fields

Field	Type	Description
placements_completed.value	string	Number of completed C-suite and VP placements ("25+")
average_close_time.majhi_group_days_low	integer	Low end of Majhi Group average close time range (30 days)
average_close_time.majhi_group_days_high	integer	High end of Majhi Group average close time range (45 days)
average_close_time.industry_median_days_low	integer	Industry median range low (65 days)
average_close_time.industry_median_days_high	integer	Industry median range high (90 days)
offer_acceptance_rate.value	decimal	Offer acceptance rate (0.90 = 90%+)
replacement_guarantee.duration_days	integer	Replacement guarantee period in days (90)

featured_case fields

Field	Type	Description
close_time_days	integer	Actual close time for this search (41 days)
prior_firm_attempts	integer	Number of prior search firms engaged (2)
prior_firm_duration_days	string	Duration of prior firm attempts ("60+")
search_value_usd	integer	Total compensation value of the role in USD (275000)

Methodology

Majhi OS data

Operational metrics — reply rates, shortlist approval rates, audit trail coverage, tool spend, close times — are drawn from direct hiring system engagements through June 2026. Each metric reflects actual system output, not projections or estimates.

Industry baselines

Industry ranges reference published benchmark research from LinkedIn Talent Solutions annual reports, SHRM recruiting benchmarks, and Greenhouse annual recruiting benchmarks. Where industry data presents ranges, both bounds are captured in _low and _high fields.

VP search stall rate

The 68% VP search stall rate past week 10 reflects patterns from Majhi OS mandate analysis combined with industry research. It is not a Majhi-only statistic — it describes the industry condition that Majhi OS is built to solve.

Failure mode recovery rates

Recovery rates with monitoring are from Majhi OS engagements where failure modes were detected within their detection window and recovery actions were executed. Recovery rates without monitoring are observed from industry patterns where the same failure modes were identified only after the mandate had already collapsed.

Data integrity guarantee: No data in the Majhi OS JSON schema is fabricated, estimated from incomplete data, or projected forward from a small sample without disclosure. Every figure is either (1) directly from documented Majhi OS engagements or (2) sourced from named published industry research. Source attribution is embedded in each data object.

Build Something With This Data

The JSON endpoints are free to use. If you build a tool, dashboard, or integration using Majhi OS data, we want to see it.

Contact Majhi OS