LogisticsApplication Modernization

Legacy TMS Modernized for Real-Time Freight Visibility

A mid-sized freight carrier ran its core TMS on a codebase that predated the smartphone era. Phased modernization extracted modules behind clean APIs while live shipments kept moving — closing the gap between batch EDI cycles and the real-time visibility shippers now expect.

15–30%
Freight cost reduction
40–50%
Fewer delivery delays
Months → weeks
Carrier integration timeline
app.freightops.com/dashboard
Legacy TMS Modernization Case Study — Logistics platform dashboard
Logistics · Application Modernization
Industry
Logistics
Services
Discovery Workshop, Application Modernization, AI Agent Development, DevOps
Founder-led engineering
Key Metrics
15–30%
Freight cost reduction
40–50%
Fewer delivery delays
Months → weeks
Carrier integration timeline
Overview

The big picture

A mid-sized freight carrier was running its core transportation management system (TMS) on a codebase that predated the smartphone era. The system handled routing, carrier management, and freight billing — the operational backbone of the business. But every new carrier integration took months instead of weeks, and the team had long since lost anyone who fully understood how the rate engine actually worked.

This is a common pattern in logistics: legacy TMS platforms batch-communicate with carriers on cycles ranging from four to twenty-four hours, while modern shippers increasingly need sub-minute data to make routing and pricing decisions. That gap is what makes legacy TMS modernization one of the highest-stakes technical decisions a logistics company can make.

Discovery WorkshopApplication ModernizationAI Agent DevelopmentDevOps
Industry
Logistics
Services
Discovery Workshop, Application Modernization, AI Agent Development, DevOps
app.freightops.com/dashboard
Freight warehouse and logistics operations
The Challenge

Where the legacy TMS held the business back

Legacy transportation management systems accumulate more than old code — they accumulate undocumented business logic the company depends on every day.

01

Interdependent core modules

Rate engine, routing logic, carrier management, and freight settlement were tightly coupled, so a change in one module could cascade into failures in another.

02

Undocumented rate complexity

Years of carrier-lane pricing, accessorial schedules, fuel surcharge indices, and customer-specific overrides existed only in the heads of a few long-tenured employees.

03

Zero tolerance for downtime

With active shipments moving at all times, a big-bang migration wasn't viable. The freight had to keep moving throughout modernization.

04

API-less architecture

The closed platform made it difficult to expose data to carrier portals, customer tracking tools, or newer logistics software without brittle point-to-point integrations.

The Approach

Phased modernization, not a rebuild

Xorora treated this as a phased application modernization — the standard, lower-risk path for logistics companies operating at volume.

01

Discovery Workshop

A structured discovery process inventoried every module, undocumented business rule, and integration point before any development began — the step most often skipped when TMS projects overrun.

02

Application Modernization

Core modules were extracted one at a time — routing, then carrier management, then freight billing — each restructured behind a clean API boundary while the legacy core kept processing live shipments.

03

AI Agent Development

An AI agent assisted with static analysis of the legacy codebase, flagging likely business-rule locations for human review and narrowing where domain expertise needed to focus.

04

DevOps

A CI/CD pipeline and staged rollout process let each extracted module ship independently and roll back quickly if a discrepancy showed up in production.

Data model

Core data model

Representative of how this class of system is typically modeled — not a reproduction of a specific client's schema.

Carrier

  • PKcarrier_id (PK)
  • ·name
  • ·contact_info
  • ·edi_interface_type
  • ·status (active / inactive)

Rate Card

  • PKrate_card_id (PK)
  • FKcarrier_id (FK → Carrier)
  • ·lane
  • ·base_rate
  • ·fuel_surcharge_index
  • ·effective_date
  • ·expiration_date

Accessorial Rule

  • PKaccessorial_id (PK)
  • FKrate_card_id (FK → Rate Card)
  • ·charge_type
  • ·condition
  • ·amount

Customer

  • PKcustomer_id (PK)
  • ·name
  • ·billing_info
  • ·contract_terms

Shipment

  • PKshipment_id (PK)
  • FKcustomer_id (FK → Customer)
  • FKcarrier_id (FK → Carrier)
  • ·origin
  • ·destination
  • ·status
  • ·created_at

Rate Line Item

  • PKline_item_id (PK)
  • FKshipment_id (FK → Shipment)
  • FKrate_card_id (FK → Rate Card)
  • ·calculated_amount
  • ·accessorials_applied

Exception

  • PKexception_id (PK)
  • FKshipment_id (FK → Shipment)
  • ·type (rate mismatch / routing conflict / billing discrepancy)
  • ·status
  • ·resolved_by
  • ·resolved_at

Relationships

  • A Carrier has many Rate Cards.
  • A Rate Card has many Accessorial Rules.
  • A Customer places many Shipments.
  • A Shipment is fulfilled by one Carrier and generates many Rate Line Items.
  • A Rate Line Item references one Rate Card.
  • A Shipment may have zero or more Exceptions.
Tech Stack

Backend, AI analysis & infrastructure

Node.js
PostgreSQL
Python
AWS
Docker
GitHub Actions
The Outcome

What phased TMS modernization typically delivers

Figures reflect published industry benchmarks for comparable TMS modernization projects, not a confirmed result from this specific engagement.

15–30% freight cost reduction
Through automated carrier selection and load optimization once modules sit behind clean API boundaries.
40–50% fewer delivery delays
Real-time visibility replaces the batch-cycle latency of a legacy system.
Integrations in weeks, not months
Carrier integrations that took months on the legacy platform become weeks-long projects on an API-first foundation.
Why This Matters

Why this matters for logistics companies considering modernization

Legacy TMS platforms aren't just slow — they cap what a logistics business can do next. Carrier integrations that should take weeks stretch into months. Real-time freight visibility becomes structurally impossible on a system built for batch EDI cycles. And every year modernization is delayed, the undocumented business logic inside the system becomes riskier to untangle.

A phased, discovery-first approach follows the same order every time: inventory before rebuild, modular extraction before full rewrite, automated deployment before big-bang release. That order is what separates a modernization project that ships safely from one that breaks carrier connectivity on day one.

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