Label Intelligence Copilot

Problem

Shipping networks move millions of packages daily, with billing based on labels tied to customer accounts. However, fraudulent label activity is rising, including:

• Reuse of valid labels across unauthorized accounts
• Misuse of programs like Global Direct Entry (GDE)
• Inconsistent scan patterns across facilities
• Fake return addresses to bypass scrutiny

Much of this data lives at the edge, scattered across scan logs, label files, and route metadata. Because of this, centralized detection is difficult and often reactive.

The impact includes:

• Significant revenue loss and chargeback risk
• Increased cost of manual audits and claims
• Undetected fraud across global shipping pipelines

What it does

Label Intelligence Copilot is a prebuilt GenAI assistant that helps agencies and logistics providers detect, investigate, and explain label misuse across distributed systems.

It empowers analysts and fraud teams to:

• Detect reused, spoofed, or misrouted labels
• Identify return address and account mismatches
• Trace anomalous scan sequences
• Correlate patterns across time, geography, and sender profiles
• Produce explainable, auditable summaries for decision-makers

How it works (Powered by SeekrFlow™)

The solution runs on a reasoning-capable agentic architecture, powered by SeekrFlow. It ingests structured scan data (markdown files, logs, manifests), retrieves relevant patterns, supplements with external context, and generates grounded insights using language models.

Planner Agent: understands fraud-focused questions like: “Which accounts had reused tracking numbers scanned at multiple origins?”

FileSearch Tool: mines markdown logs from edge systems for label, scan, and account data

WebSearch Tool: augments investigations with insights about known global fraud methods

LLM-RAG Tool: provides natural language explanations grounded in file data — not guesses

Thread-Aware Memory: enables analysts to follow up, revise queries, and trace prior evidence

Key capabilities

• Accepts prompts like: “Which accounts shared label IDs across different facilities?”
• Retrieves and analyzes label, scan, and routing data from files
• Enriches context with public policy references and online fraud indicators
• Explains suspicious activity in natural language, with traceable logic
• Supports follow-ups and iterative analysis across investigations

Ideal users

Postal Services: Validate label use, prevent billing fraud

Customs & Border Protection: Monitor international entry schemes

Inspector General Offices: Investigate systemic or organized misuse

Global Logistics Providers: Detect internal and third-party label anomalies

E-Commerce Platforms: Identify account abuse and return fraud

Retailers: Monitor misuse tied to return addresses and refunds

National Security Orgs: Track patterns tied to illicit networks

Built on SeekrFlow

Edge-Compatible Data Engine: Parses scan logs, label files, and routing data from decentralized systems

FileSearch: Searches markdown-based scan events and historic fraud patterns

WebSearch: Retrieves policy references and external fraud indicators (e.g. GDE misuse)

LLM-RAG: Generates grounded, natural language explanations for suspicious label behavior

Agent Framework: Planner and Evaluator agents coordinate multi-step fraud investigations

Session Memory: Maintains context across analyst queries and follow-ups

UI or SDK Access: Supports visual analysis or programmatic workflows

Value

• Detects non-obvious, emerging fraud tactics
• Delivers explainable, reasoning-capable results
• Reduces manual audit and investigation overhead
• Enables proactive fraud prevention at the edge
• Adaptable to evolving USPS policies and fraud strategies

Where you can extend it

• Integrate with billing systems to block fraudulent charges
• Train on historical fraud cases for improved precision
• Add dashboards for visual scan route anomaly detection
• Expand data inputs to include images, customs forms, or metadata
• Refine reasoning models with internal audit policies

Solution traits

Reasoning-Capable | Agentic | Retrieval-Augmented | Edge-Aware | File-Based Inference | Investigative-Ready | Multi-Turn Query Support | Explainable | Auditable | Extendable

Takeaway

Label Intelligence Copilot transforms fragmented scan data into actionable intelligence. It gives agencies and logistics leaders a scalable, explainable, and adaptable system to identify and investigate label misuse—not after delivery, but as it unfolds.