How the continuous intelligence loop is built: data ingestion, AI reasoning, integration topology, and deployment model
Expert-in-the-Loop Design
From Systems of Record to Systems of Action
ProcurementIQ sits above existing ERP, PLM, and procurement platforms — adding contextual intelligence, recommendation engines, and automated escalation without replacing any system of record. Digital coworkers continuously monitor, correlate, and recommend across connected data sources.
Continuous Intelligence Flow
Data Ingestion
ERP, PLM, Portals
→
AI Signal Detection
Pattern & anomaly
→
Recommendation Engine
Owner + task + path
→
Expert Decision
Accept, override, escalate
→
Outcome Capture
Learning loop closes
Layer 1 — Data
Connected Source Systems
ProcurementIQ ingests from existing systems of record — no data migration required. Digital coworkers continuously poll for signals across procurement, finance, and engineering platforms.
SAP S/4HANAOracle ERPCoupaAribaTeamcenter PLMWindchillServiceNowSupplier PortalsContract Repositories
Layer 2 — Intelligence
AI Signal Processing & Context Engine
Multi-model AI stack performs real-time anomaly detection, cross-domain pattern clustering, root cause analysis, and recommendation generation with explainable confidence scores.
Signal DetectionPattern ClusteringRCA EngineKnowledge GraphConfidence ScoringCorrelation Analysis
Layer 3 — Decision
Expert-in-the-Loop Recommendation Layer
AI recommends initiative owners, task assignments, and closure paths. Experts accept, override, or escalate. Every decision — and override — becomes a training signal for the next recommendation cycle.
Initiatives and tasks are tracked via a two-tier Kanban. Automation opportunities are identified by AI and surfaced for approval. Digital coworkers can execute approved automations across connected systems.
Every initiative outcome, expert correction, and override is captured and fed back into the AI model. The system maintains explicit audit trails of what worked vs. what didn't, continuously improving recommendation quality without requiring manual retraining cycles.