Transform manual discrepancy management for Meta product returns into an AI-powered automated process. Covers D2C unit-level returns, B2B bulk shipment receiving, and dock log / trailer-level quantity verification.
The current process from dock to disposition — each step is largely manual and sequential, with non-value-add dwell time that the AI system will eliminate or minimize.
FedEx truck arrives at dock
FedEx scans package tracking ID
Package placed on flex conveyor
Operator scans tracking ID + RMA into Tracking Tool
Tracking Tool requests FedEx data (reference #, weight, dimensions)
Operator performs Dock Log — bulk upload to Plus
Package moved to receiving line
Receive Operator performs system receiving
Exception → Discrepancy ticket in Plus → Problem Log
Eight critical issues driving the transformation — each one maps to specific future-state automation.
Impact: Manual copy/paste; data entry errors; duplicated effort
Root Cause: No API or automated sync between Plus and Jira
Impact: Institutional knowledge lost; inconsistent execution
Root Cause: Complex manual process dependent on tribal knowledge
Impact: Aging inventory; inaccurate open case counts
Root Cause: Closure requires manual CSR action; no auto follow-through
Impact: Data security exposure; compliance risk
Root Cause: Problem Log overflow; units stored outside secure area
Impact: Cannot predict or auto-resolve common cases
Root Cause: Meta's adjudication criteria not shared with RXT
Impact: Every ticket requires manual back-and-forth
Root Cause: No trailing-period analytics on Meta responses
Impact: Miscounts; delayed discrepancy detection
Root Cause: No camera or vision-based validation at dock
Impact: Delayed discrepancy identification
Root Cause: No vision model to read box labels or count
Eight interconnected components that transform the discrepancy management process from manual to intelligent automation.
System instantly creates match/mismatch events when Serial #, RMA, and Airway Bill are scanned. On mismatch, a Jira ticket is auto-created with full discrepancy context (SN, RMA, model, mismatch type, product photos).
Bidirectional API integration eliminates manual copy/paste. Jira ticket data is simultaneously transmitted to Plus in real time for claims tracking and insertion into the 2x/day Meta submission.
Trailing 30/60/90-day analysis of Meta response patterns enables automatic disposition for high-confidence cases. 'New RMA Assigned' and 'Scrap' dispositions are handled automatically when confidence is high.
AI vision model inspects unit condition. If hazardous goods criteria are met and no Legal Hold applies, scrap is auto-initiated. Historical scrap records train and validate classification accuracy.
Cameras scan box labels at point of receipt. System extracts model, quantity, and part number data and compares against expected RMA manifest. Discrepancies reported to Meta in real time.
Hard-mounted cameras in each dock bay with system-driven triggers capture images and record metadata. Vision model auto-counts and cross-references manifest data. Damaged pallets auto-trigger Meta notification.
AI agent closes records in both Jira and Plus upon disposition, removing serial numbers from aging inventory reports. Material handlers receive automated pull tickets with rack location and routing instructions.
Automated routing ensures units stay in secure area until data wipe is confirmed. Eliminates the current security gap of un-wiped units in outside storage.
Structured touchpoints ensuring Meta has full visibility and opportunity to provide feedback at every stage of the transformation.
Scheduled review sessions where Reconext presents implementation progress, auto-adjudication accuracy metrics, and upcoming milestones. Meta provides feedback on priorities and rule refinements.
During Phase 3, auto-adjudication runs in parallel with manual process. Meta reviews AI decisions vs. their actual responses to validate accuracy before going live.
Meta shares decision rules for common dispositions (New RMA, Scrap, Return to Customer). These rules are encoded into the auto-adjudication engine with Meta's approval.
Joint working session to define Legal Hold vs. No Legal Hold determination criteria — the key gate for automated scrap disposition.
Meta receives a generative AI interface to query discrepancy data, resolution patterns, and aging metrics directly — reducing back-and-forth requests.
Automated reports showing auto-adjudication accuracy, aging reduction, cycle time improvement, and labor savings — shared with Meta on a defined cadence.
Every Dream Team member has explicit, actionable responsibilities for this project. No ambiguity — here is exactly what each role owns.
Presents auto-adjudication accuracy metrics to Meta; negotiates rule refinements based on model performance data
Provides Meta with API documentation for the bidirectional sync; troubleshoots data flow issues
Coordinates with Meta on disposition workflow changes; ensures Meta's response SLAs are met
Primary technical contact for Meta on scrap criteria, Legal Hold rules, and quality standards
Primary relationship manager with Meta program team; owns the communication cadence and escalation path
| # | Open Item | Owner | Priority | Blocker |
|---|---|---|---|---|
| 1 | Define Legal Hold vs. No Legal Hold determination criteria | Meta / RXT Joint | High | Blocks scrap auto-adjudication |
| 2 | Confirm B2B box labels are consistently scannable across all suppliers | RXT Operations | High | Blocks B2B vision pilot |
| 3 | Obtain historical scrap records for vision model training | Meta | Medium | |
| 4 | Camera hardware selection and dock installation logistics | RXT Facilities | Medium | |
| 5 | Plus API integration specifications for real-time Jira sync | RXT IT / Meta | High | Phase 1 dependency |
| 6 | Obtain Meta's adjudication decision rules to enable auto-adjudication | Meta | High | Blocks auto-adjudication |
| 7 | Define secure storage protocol for un-wiped discrepancy units | RXT Operations / Security | High | Compliance risk |
| 8 | Address aging discrepancy case backlog in Plus | RXT CSR Management | High | Immediate action |
NVIDIA demonstrates how visual AI agents are transforming semiconductor manufacturing with automated defect detection and root cause analysis using vision foundation models.
Real-world examples of camera-based receiving, counting, and label scanning in logistics operations.