Majhi OS doesn't just monitor your hiring system.
It predicts where it is about to break.
The Failure Prediction Engine analyzes patterns across every active mandate and surfaces stall probability before the stall occurs — giving your team 7 to 21 days to run recovery before the search collapses.
What It Detects
Five failure patterns Majhi OS flags before they become crises.
Mandate Overload
A recruiter carrying more mandates than their historical close-rate performance supports. Overload doesn't fail slowly — it fails suddenly. Flagged 10–14 days before quality degrades.
Funnel Collapse
Pipeline conversion dropping below baseline stage by stage. A 15% drop in any single-stage conversion rate triggers a funnel collapse alert before top-of-funnel runs dry.
Outreach Decay
Reply rate declining week-over-week before the team notices. When decay crosses the 20% threshold, the prediction engine flags the sequence — and surfaces the alternative approach most likely to reverse it.
Candidate Disengagement
Response latency patterns that precede ghosting by 7–14 days. The system detects engagement decay before a candidate goes dark — creating a closing intervention window.
SLA Breach Risk
Predicted timeline to close versus committed timeline — surfaced 10+ days before the miss. Lets leadership realign expectations or adjust resources before the breach becomes a crisis.
Recovery Playbook Trigger
When any failure signal crosses threshold, the system doesn't just alert — it surfaces the Recovery Playbook most likely to reverse the specific pattern detected. Specific, sequenced, actionable.
Recovery Playbooks
The system learns what actions recover mandates.
Recovery Playbooks are not generic advice. They are system-learned sequences built from operational data across hundreds of mandates — specific actions, sequenced by impact probability, matched to the exact failure pattern detected.
"When the prediction engine flagged outreach decay on the VP Engineering search, the recovery playbook recommended: pause current sequence, pivot to second-degree network activation, reframe outreach around current product launch. Reply rate recovered from 9% to 28% in 8 days."
Over time, the Failure Prediction Engine gets smarter. Every mandate that runs through Majhi OS adds to the operational intelligence graph — improving prediction accuracy and playbook effectiveness with each cycle.
The Intelligence Graph
What Majhi OS learns as it runs.
Most software helps teams execute tasks. Majhi OS learns from every task executed. The operational intelligence graph accumulates across every mandate, every recovery, every close — building prediction accuracy that compounds over time.
Industry Patterns
Which industries have higher response decay rates at which outreach stages. Pattern-matched playbooks for SaaS versus fintech versus DevTools.
Recruiter Profiles
Which recruiter behaviors correlate with high close rates at which mandate types. Enables better mandate assignment and early overload detection.
Timing Intelligence
Which outreach timing patterns drive highest reply rates by seniority level, industry, and sequence position. Applied automatically to all active sequences.
Related Reading
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Bring your hardest active mandate.
In a 45-minute Mission Walkthrough, we'll run the Failure Prediction Engine against your current search — live, using your actual data.
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