Freehand vs Trax Technologies

Loop Shows You What's Wrong. Freehand Recovers the Money

For Fortune 500 logistics and supply chain leaders managing multi-modal, multi-currency freight at scale, sampling isn't an audit strategy, it's a liability. Freehand delivers 100% pre-payment coverage with 4-way matching across every carrier invoice, every mode, every billing cycle. No exceptions left unresolved.

50%
of the cost
5X
accuracy
5 days
to proven results
Trusted by global leaders in Logistics, Manufacturing, and Retail
Awards and Recognitions
Why choose Freehand?

Where Freehand Wins. Where Trax Falls Short.

Five dimensions that separate autonomous freight audit from a detection-only analytics platform — and determine whether an exception gets resolved or just reported.

Limitations
Advantages
Prizma Detects — Your Team Still Resolves
Prizma surfaces exceptions. The resolution workflow — classifying the discrepancy, assembling evidence, communicating with the carrier, tracking the response, filing the dispute before the window closes — still goes to your team. Detection is the output. Action is still the input.
Detection and Resolution — Not Detection Alone
The Exception and Dispute Agent does not surface exceptions for your team to handle. It classifies the discrepancy, assembles the supporting evidence, communicates with the carrier, and tracks resolution to closure — autonomously, within the carrier's dispute window.
Shared AI Model — Trained on Everyone's Data
Trax's 25-year data asset is a differentiator for Trax. A model trained across all clients produces recommendations based on patterns from hundreds of companies whose freight networks may not resemble yours. Your rules are inputs to a shared model — not a model built for you.
Per-Account Context Graph — Your Data Only
The audit model is built exclusively from your contracts, your carriers, your billing history, your exception patterns. No other customer's freight data influences how your invoices are audited. It gets smarter as your specific data accumulates — not as someone else's does.
Exception Backlog With No Structured Resolution
Zero metrics on why exceptions occurred. Zero tracking of resolution cycle times. Zero analytics on carrier billing patterns by root cause. Thousands of shipment-level exceptions accumulate in backlog. The dashboards show what happened — nobody has time to act on it systematically.
GL Coding Automated at Line Level
GL Coding Agent handles all allocation at invoice line level. 200+ rules configurable and self-correcting. Multi-dimensional structures — cost centers, legal entities, regions — processed without manual intervention per invoice.
Manual GL — Every Invoice Requires Intervention
Multi-dimensional GL structures with 200+ rules require manual intervention for every single invoice allocation. Finance and logistics operate in disconnected silos. System mismatches require constant manual reconciliation between TMS, ERP, and the audit platform.
Rate Manager Agent — Live, Versioned Contracts
Contracted rates held in a live, version-controlled repository. Audit always runs against the current contract. When a carrier updates its billing schedule, the Rate Manager updates automatically — without someone uploading a new rate file.
Agentic AI Is a Roadmap — Not a Live Capability
Trax has disclosed an agentic AI roadmap. The question is which workflows become autonomous, on what timeline, and with what human oversight required. The answer matters: 60% of audit effort remaining manual despite AI positioning is a production outcome, not a roadmap gap.
Agentic Execution in Production Today
Audit Agent, Exception and Dispute Agent, Rate Manager Agent, Spend Intelligence Agent, GL Coding Agent — all running live freight audit operations at Fortune 500 accounts. Not on a roadmap. Not in a product deck. In production.

Prizma is Trax's analytics layer. The question is what happens after the insight. Who acts on it, and how fast? In our case, the answer was our team — manually — after the platform surfaced the problem. That's not the same as fixing it.

Global Logistics Finance Leadership — Fortune 50 Global Biopharma Company
Case Studies

100% Coverage. BPO Replaced. Same Team.

Real outcomes from enterprises that have replaced BPO-led freight audit with Freehand's AI Teams.

Case Study 01

Fortune 100 Consumer Electronics Company

A global consumer electronics manufacturer with over $2B in annual freight spend processed across multiple ERPs and business units. Freight audit ran through a BPO on a sample basis. GL coding errors, accessorial overcharges, and duplicate billings were not being systematically caught or recovered.

Fortune 100 . Consumer Electronics . Manufacturing

$3M+

In annual freight overcharges recovered

100%

Invoice audit coverage across $2B in freight spend

‍

  • Full transition from sample-based BPO audit to 100% AI-powered coverage across all modes
  • GL coding automated across all business units and geographies. Finance closed on accurate data.
  • 60% reduction in freight management effort; carrier payment on-time rate reached 100%
Case Study 02

Leading U.S. Industrial Manufacturer

$400M+ freight spend. AP teams spending 2–3 hours daily cleaning invoice files before audit could begin.

$400M+ Freight Spend · Industrial Manufacturing

6%

Combined freight savings across all modes

$15M+

Annual freight cost optimization

  • Invoice validation agent deployed in days, connected to ERP, EDI feeds, and vendor portals on day one
  • Every invoice structurally clean before reaching the audit queue, no manual parsing required
  • Duplicate billings, missing GL codes, and vendor ID errors caught automatically across all spend categories

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AI Teams

Meet the Agents Working Your Freight Invoices 24/7

Freight payment spans GL coding, disbursement, accruals, and spend reporting, each step a manual handoff in traditional finance operations. Freehand deploys one AI Team with four specialized agents, each owning a distinct part of the payment lifecycle.

Step 01
IA

Invoice Audit Agent

Validates every invoice line against contracted rates across parcel, LTL, FTL, ocean, and air. Runs 3-way and 4-way matching. Flags discrepancies across 30+ error types in a single pass.

100% multi-modal invoice coverage, zero sampling
Step 02
DM

Dispute Management Agent

Files structured overcharge disputes directly with carriers for every confirmed discrepancy. Tracks resolution status, escalates aged disputes, and logs every carrier response against the original claim.

Automated dispute filing, no analyst queue
Step 03
GL

GL Coding Agent

Assigns cost centers, GL accounts, and business units using rules mapped to your chart of accounts. Complex cost splits across divisions, brands, and geographies handled at invoice time.

Zero manual GL entries post-audit
Step 04
SI

Spend Intelligence Agent

Classifies every audited invoice by mode, carrier, lane, cost center, and charge type. Delivers a finance-grade, queryable freight spend layer accessible at any point in the month.

Finance-grade audit data, any point in the month
benefits

Measurable Outcomes from Week One

Outcomes measured from live deployments across Fortune 500 freight audit and payment operations.

$6B+
freight spend managed
1.8B+
annual freight transactions
100%
invoice audit coverage

Every invoice line is audited against the contracted rate, parcel, LTL, FTL, ocean, and air, without a human reviewer in the loop.

Accessorial overcharges, fuel adjustment errors, and detention billing discrepancies are caught and disputed in the same audit cycle they occur.

GL coding and cost allocation run automatically at audit time. Finance closes the month on verified freight cost data, not estimates.

Dispute resolution runs without a human queue. The Dispute Management Agent files, tracks, and escalates. Carriers receive structured claims on a defined timeline.

Every recovered dollar is traceable invoice, contracted rate, billed amount, dispute outcome, and payment record in one place.

Freight spend data is classified by mode, carrier, lane, and cost center. Finance-grade and available at any point in the month.

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If the platform surfaces the problem
but your team still resolves it — what are you paying for?