Adaptive Recommendation Engine
AI-ranked action queues updated in real time from all 9 engines, with confidence scores and full audit trails on every recommendation.
Adaptive Action Queue
Sample AI output
Priority queue updated: 3 high-confidence manager actions queued for this week. Top item: 'Retention risk check-in with Alex M.' (confidence: 0.94, urgency: high). Estimated time to complete all 3: 22 minutes.
Input signals
- AI confidence scores from all modules
- Role changes and team reshuffles
- Sentiment shifts
- Assessment completions
- Manager action history
What RUDY surfaces
- Ranked action queue per user
- Confidence score per recommendation
- Audit trail of recommendation rationale
- Priority updates in real time
How it works
ARPF aggregates signals from all engines
The Adaptive Recommendation & Prioritization Framework collects confidence-weighted signals from all 9 RUDY AI engines and composes a unified action queue per user.
Action queue ranked by impact × confidence
Each recommended action is ranked by the product of predicted impact and signal confidence — surfacing the highest-value, most certain actions first.
Queue updates in real time with full audit trail
As signals shift, the queue reprioritizes automatically. Every recommendation carries an audit trail: signal source, confidence score, rationale, and timestamp.
A day with RUDY
“Monday morning, an HR manager opens RUDY. ARPF shows 4 prioritized actions: adjust workload for Maya (94% confidence), re-pair a team after chemistry signal (88%), schedule an early review for Alex (81%), suggest a mentor for Priya (73%). Each recommendation is auditable, explainable, and actionable in two clicks.”
Research foundation
Multi-armed bandit algorithms and reinforcement learning for adaptive recommendation systems.
Frequently asked questions
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No surveillance. No black-box scoring. Human review where it matters.