The newly released 2026 Gartner® Market Guide for Freight Audit and Payment Providers highlights a transformative shift in the industry: the rising use of AI to accelerate the speed and quality of audits. However, as we transition into a Software 2.0 and 3.0 world, the conversation must evolve far beyond traditional invoice checking. We are no longer just talking about a freight audit; we are talking about a fundamental data audit.
For modern finance and supply chain leaders, legacy software was never designed to handle the dynamic nature of global logistics or its highly fragmented data. The supply chain has a severe data problem. Across the physical economy—where we deal in atoms, not just bits—information remains dangerously siloed. Unstructured data trapped in PDFs, legacy EDI feeds, and disjointed spreadsheets creates relentless noise and massive margin leakage. The future of enterprise technology is no longer just about writing code; it is about providing the right data to let advanced systems excel. Models are only as good as the data feeding them.
The future of enterprise technology is no longer just about writing code; it is about providing the right data to let advanced systems excel. Models are only as good as the data feeding them.
This is precisely why bolting off-the-shelf AI onto legacy processes is merely a band-aid. What is required is a native, full-stack AI approach that attacks the fundamental data problem at its root. The freight audit and payment (FAP) industry sits at the intersection of more than $11 trillion in spend between manufacturers, retailers, carriers, and suppliers—exactly where messy data creates massive friction.
Loop is tackling this problem. By unifying transportation, logistics, and financial data, Loop empowers organizations to build a clean, reliable data environment. Their proprietary AI model, DUX™, understands transportation domain language, extracting, cleansing, and normalizing data across the end-to-end audit and payment lifecycle.
This progression—from unstructured to structured data, from disconnected tasks to AI and agents—cannot be shortcutted. By establishing a pristine data foundation first, companies gain a digital twin of their supply chain. What makes Loop's approach distinctly powerful is that this twin is not built in isolation.
Loop maintains a shared supply chain ontology trained across its experience with countless shippers, embedding deep knowledge of carrier idiosyncrasies, document structures, and logistics nuances. Layered on top is a business ontology shaped by contracts, rates, lanes, addresses, and policies. This is precisely what separates Loop from AI solutions that lack true domain expertise. Loop’s foundation empowers AI agents to deliver deep decision intelligence and critical scenario modeling—answering questions like, "What happens to our cost-to-serve if we change this carrier or adjust this lane?"
When you solve this underlying data problem, the real-world financial outcomes are immediate and profound. AI in the supply chain is about acceleration, not replacement—designed to augment existing infrastructure and fix critical operational issues. A global food company moved from auditing just 10% of their bills of lading to a full 100% audit rate. One of the fastest growing beverage companies achieved over 90% auto-approvals on invoices, preventing over $600,000 in overpayments.
This clean transportation data acts as a powerful competitive differentiator, giving finance teams immediate clarity and solving historically messy cost allocation and inadequate risk reporting.
A clean, accurate data foundation also unlocks the next frontier: Agentic AI. Loop views the agentic layer as a perfect complement to existing teams, helping drive key outcomes that lean logistics teams need help delivering. The first is people efficiency—eliminating the administrative burden on back-office teams through audit automation, communication support, and dispute resolution. The second is network efficiency—deploying agents that monitor network health, flag stale contracts, and identify carriers consistently underperforming. The third is decision-making validation—ensuring teams always make optimal choices by alerting them to behavioral patterns that signal errors, such as a wrong freight class or inefficient shipping mode. Together, these three pillars will accelerate a shift from reactive logistics management to a proactive, intelligence-driven operation.
Ultimately, this AI-driven approach is about layering intelligence over existing systems, not replacing them. Loop takes the fragmented operational data an enterprise already has, transforms it into a governed structure, and uses it to accelerate margin discipline and business excellence.
Over the past year, Loop's merger with Data2Logistics and acquisition of StrategIQ Commerce have expanded their capabilities across parcel management, real-time visibility, and global payment execution. Loop was recently recognized in the 2026 Gartner® Market Guide for Freight Audit and Payment Providers—validation of their vision to industrialize services and unlock the value trapped in the physical economy. But, as CTO Shaosu Liu summarized: "We're thrilled to be recognized, but we're just getting started."
