The Customer

The customer is a logistics and supply chain platform responsible for evaluating shipment and delivery conditions across multiple operational scenarios. Shipment classification depends on multiple variables—dimensions, weight, volume, distance, and delivery categorization—all of which must be evaluated accurately and in real time.

These decisions directly impact routing, pricing, delivery timelines, and operational efficiency. As shipment volume and rule complexity increased, the need for a flexible and scalable decisioning system became critical.

What Triggered the Change

Delivery and shipment logic was embedded deeply in application code. Any modification—whether adjusting thresholds, adding new categories, or updating routing rules—required engineering effort and deployment cycles.

As operational requirements evolved, this approach introduced friction. Business teams could not adapt rules quickly, and engineering teams became a bottleneck for routine updates.

At the same time, the system needed to process decisions in real time without compromising latency. Scaling rule complexity while maintaining performance exposed clear architectural limitations.

The Challenges

Hardcoded Rule Logic

Shipment classification logic lived inside application code, making updates slow, risky, and dependent on engineering cycles.

Limited Business Control

Non-technical teams could not configure or modify rules directly, slowing response to operational changes.

Complex Multi-Variable Decisioning

Rules depended on multiple inputs such as weight, volume, distance, and categories, requiring structured and branching logic that was difficult to manage in code.

Lack of Standardization

Inputs and rule definitions lacked consistent structure, making reuse and governance difficult.

Deployment and Portability Constraints

The system required flexibility for self-hosted deployment, white-labeling, and rule portability across environments.

Performance Requirements

All decisions needed to execute in real time with low latency to avoid impacting shipment processing workflows.

Why the Previous Approach Fell Short

As rule complexity increased, the limitations of a code-driven approach became more visible.

Engineering teams were responsible for implementing and maintaining business logic they did not fully own. Business teams understood operational requirements but could not directly translate them into system behavior.

This disconnect slowed iteration, increased deployment overhead, and introduced risk with every change. At the same time, scaling rule complexity made it harder to maintain performance and consistency.

Decision logic, instead of enabling flexibility, became a constraint on operational speed and scalability.

What the Customer Needed

The platform required a system that could:

• Externalize shipment and delivery rules from application code
• Execute decisions in real time with low latency
• Support complex, branching decision logic
• Enable non-technical users to manage rules safely
• Standardize inputs and rule definitions
• Provide rule portability and exportability
• Support self-hosted, scalable deployment architectures

The Solution: How Nected Was Implemented

The customer implemented Nected as a centralized decisioning layer for shipment classification and delivery logic.

Real-Time API-Based Decision Execution

Shipment rules are executed through API calls in real time, allowing immediate classification based on current input data.

Rule Tables, Workflows, and Decision Trees

Complex logic is modeled using structured decision tables and branching workflows, making rules easier to understand, maintain, and extend.

Business-Owned Rule Management

Non-technical users can configure and update rules directly, reducing dependency on engineering for routine changes.

Dictionary-Based Schema Governance

Inputs are standardized using dictionary-based schema control, ensuring consistency across rules and reducing ambiguity in decision logic.

Kubernetes-Based Scalable Deployment

Nected supports self-hosted deployment using a Kubernetes-friendly architecture with worker-based execution, enabling horizontal scaling and cost-efficient infrastructure.

Rule Portability and White-Labeling

Rules can be exported, imported, and embedded across environments, supporting white-label use cases and multi-tenant deployments.

Before vs After

Quantitative Outcomes

• Sub-100ms shipment decision latency
• Real-time rule execution enables immediate shipment classification without impacting system performance.
• 100% externalization of decision logic from code
• All shipment rules are now managed outside application code, reducing deployment overhead and engineering dependency.
• Scalable rule architecture for high-volume operations
• The system supports growing rule complexity and shipment volume without degradation in performance or maintainability.

Qualitative Outcomes

• Faster operational iteration
• Business teams can update rules without waiting for engineering cycles.
• Improved consistency and governance
• Standardized schemas and versioned rules reduce ambiguity and errors.
• Reduced engineering overhead
• Engineers focus on core platform development instead of maintaining business logic.
• Deployment flexibility
• Self-hosted and white-label capabilities support diverse operational environments.

Why the Customer Chose Nected

The decision was driven by the need to combine flexibility, performance, and governance in a single decisioning layer.

Nected allowed the organization to move fast on rule changes without compromising system stability or control. Business teams gained direct ownership of decision logic, while engineering teams retained confidence in scalability and architecture.

• Structured decision modeling for complex logistics rules
• Business-friendly configuration without code dependency
• Low-latency, API-first execution model
• Kubernetes-based scalable deployment architecture

Together, these capabilities provided a foundation for real-time, scalable shipment decisioning without increasing operational complexity.