You cannot build intelligence without a data model. The Majhi OS Hiring Data Model is the architectural foundation of every operational insight the system produces.
The Hiring Data Model is the operational data architecture that defines what Majhi OS measures, how it measures it, and how measurements relate to each other across mandates, recruiters, candidates, and market signals. It is the structural foundation that makes every Majhi OS insight possible — from mandate health scores to failure predictions to autonomous recovery decisions.
The Majhi OS Hiring Data Model spans five operational data domains:
Scope definition, timeline parameters, compensation range, hiring manager profile, decision velocity, and historical performance against similar mandates.
Profile signals, outreach engagement history, interview performance indicators, offer probability, and behavioral pattern data from the engagement process.
Mandate load, activity patterns, outreach effectiveness, shortlist quality, historical performance by search type, and capacity utilization.
Funnel conversion rates by stage, velocity at each transition, pipeline depth, decay rates, and historical benchmarks for equivalent mandates.
Compensation benchmarks, talent availability signals, competitive search activity, response rate baselines, and market-specific difficulty indicators.
The Hiring Health Score — the primary operational metric of Majhi OS — is a composite calculation drawn from all five data domains, weighted against historical benchmarks, adjusted for market conditions.
“A Hiring Health Score that is not grounded in a rigorous data model is a vanity metric. Ours is a composite calculation from five data domains validated against mandate outcomes across hundreds of searches.” — Manas Majhi
The Majhi OS data model is not just an architectural artifact — it is the operational foundation of the entire system. Without the data model, there is no telemetry. Without telemetry, there is no intelligence layer. Without intelligence, there is no autonomous execution.
The Hiring Data Model is the operational data architecture that defines what Majhi OS measures, how it measures it, and how measurements relate across mandates, recruiters, candidates, and market signals.
Mandate data (scope, timeline, history), candidate data (profile, engagement, behavioral signals), recruiter data (load, performance, capacity), pipeline data (funnel velocity, depth, conversion), and market data (compensation benchmarks, talent availability, competitive signals).
The Health Score is a composite calculation drawn from all five data domains — weighted against historical benchmarks, adjusted for market conditions, and calibrated by mandate-specific factors.
No. Client mandate data, candidate data, and recruiter performance data are isolated per client. Pattern learning from aggregate anonymized data improves the intelligence layer, but individual client data is never shared.
As more mandates run through Majhi OS, the model accumulates historical benchmarks for failure patterns, recovery action effectiveness, and recruiter performance correlations.
We use your actual mandate as working context. Book a 45-minute Mission Walkthrough and see what operational intelligence looks like for your specific hiring system.
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