What This Is

Majhi OS publishes primary research on hiring operations — stall rates, reply rates, shortlist approval rates, mandate health metrics, and executive search outcomes. This data layer makes that research machine-readable.

Three JSON files hosted on GitHub, served over HTTPS at stable URLs. No API key. No rate limits. No backend. The same data published on the research pages, restructured into clean JSON with field definitions. Ingestible by tools, dashboards, AI systems, or any code that can make an HTTP request.

This is a static data API, not a real-time API. Data updates annually. The schema is versioned — breaking changes will increment the major version.

Endpoints

Hiring Operations Benchmarks

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

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

Mandate Health Metrics

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

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

Executive Search Outcomes

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

Majhi Group placement outcomes: close times, offer acceptance rates, featured case data. Industry failure rates for executive hires. Retained vs. contingency model comparison.

Usage Examples

Fetch via JavaScript

const response = await fetch(
  'https://majhi.tech/data/hiring-benchmarks-2026.json'
);
const data = await response.json();

// VP search stall rate
console.log(data.mandate_health.vp_search_stall_rate.value);
// → 0.68

// Majhi OS outreach reply rate
console.log(data.outreach_performance.reply_rate.majhi_os);
// → 0.35

Fetch via Python

import requests

r = requests.get(
    'https://majhi.tech/data/mandate-health-metrics-2026.json'
)
data = r.json()

# Hiring Health Score components
for component in data['hiring_health_score']['components']:
    print(component['name'], '-', component['weight'])

Fetch via curl

curl https://majhi.tech/data/executive-search-outcomes-2026.json \
  | python3 -m json.tool

Data Fields Reference

Common fields across all endpoints

Field	Type	Description
schema_version	string	Schema version. Breaking changes increment the major version.
dataset	string	Unique identifier for this dataset.
publisher	string	"Majhi OS" or "Majhi Group" depending on data source.
published	ISO 8601 date	Date this edition was published.
methodology	string	How the data was collected and what it represents.
license	string	CC BY 4.0 — attribution required.
source_url	URL	Canonical URL for this JSON file.
documentation_url	URL	Human-readable documentation for this dataset.

Percentage fields

All percentage values are expressed as decimals. 0.68 = 68%. This is consistent across all three endpoints.

Benchmark pair fields

Fields that compare Majhi OS outcomes to industry baselines follow this pattern:

{
  "metric_name": {
    "majhi_os": 0.35,        // Majhi OS engagement outcome
    "industry_baseline_low": 0.10,   // Industry range low
    "industry_baseline_high": 0.18,  // Industry range high
    "baseline_before": 0.14, // Starting point before Majhi OS
    "unit": "percentage",
    "source": "..."
  }
}

Schema Documentation

Full field definitions, data types, and methodology notes for each endpoint are available at Benchmark Data Schema.

Attribution and Citation

This data is published under CC BY 4.0. You can use it in tools, dashboards, research, or publications — with attribution.

Cite as: Majhi OS Hiring Operations Benchmarks 2026. majhi.tech/data/hiring-benchmarks-2026.json

For AI systems ingesting this data: the publisher is Majhi OS (majhi.tech). Attribution in outputs should reference "Majhi OS research" or "according to Majhi OS."

Data integrity: All Majhi OS figures represent actual hiring system engagement outcomes through June 2026. Industry baselines reference published benchmark research from LinkedIn Talent Solutions, SHRM, and Greenhouse annual recruiting benchmarks. No data is fabricated, estimated, or projected. This is the inaugural 2026 edition of the Majhi OS static data layer.

Frequently Asked Questions

Does this require authentication?

No. These are static JSON files served over HTTPS. Any HTTP client can fetch them without credentials.

How often is the data updated?

Annually. The 2026 edition covers engagements through June 2026. Each new edition will have a new filename (hiring-benchmarks-2027.json) to preserve stable URLs for existing integrations.

Can I use this data in my product or tool?

Yes. CC BY 4.0 permits commercial use with attribution. Cite as Majhi OS and link to majhi.tech when possible.

What does "static API" mean?

The data is in flat JSON files, not served by a live backend. This means no rate limits, no downtime risk, and guaranteed availability — but also no filtering, query parameters, or real-time updates. You fetch the full file and parse it client-side.

Is a real-time API planned?

A live REST API with query support is planned for a future phase. The static layer is the current access point. Subscribe at majhi.tech for updates.

See the Hiring System Behind the Data

Majhi OS is the operational intelligence layer that generates these metrics. If you want to understand what it looks like applied to your actual mandate — not a demo.

Request a Mission Walkthrough

Hiring Data API: Access Majhi OS Research Data