Case studies
Logistics
Logistics – Regional Parcel Hub: Sortation Anomaly & Labor Allocation AI
Timeline: Build: 11 weeks. Deploy: shift-by-shift. Optimization: 8 weeks peak profiles.
Business overview
Industry: parcel hub. Complexity: peak seasonality.
Common pain points
Late mis-sort detection, jam cascades, reactive maintenance.
Full AI automation system (PrimeAxiom solution)
Vision Agent, Predict Agent, Maintenance Agent, Labor Agent.
Automation workflow (detailed system flow)
1. Scan stream. 2. AI flags anomalies. 3. Chute load predicted. 4. Tickets created. 5. Crew rebalanced. 6. Human override. 7. Learning loop.
AI agents & logic
Detect Agent, Jam Agent, CMMS Agent.
Technical architecture
Low-latency inference, hub historian, alerting.
Tools & integrations
Sorter APIs, SCADA taps, PostgreSQL, OpenAI, CMMS
Results
Mis-sort rate −63%; unplanned downtime −38%; maintenance response −52%; throughput per hour +11%.
Timeline
Build: 11 weeks. Deploy: shift-by-shift. Optimization: 8 weeks peak profiles.