Loop Unveils AI Platform to Unify Fragmented Supply Chain Data

San Francisco-based AI company Loop has unveiled an expansive Logistics Data Platform (LDP) designed to tackle the fragmented data problems that have plagued supply chain operations for decades. The launch follows a $95 million Series C funding round led by Valor Equity Partners and the Valor Atreides AI Fund.

The announcement comes from FreightWaves, which detailed the platform's capabilities and the company's strategic shift toward autonomous logistics execution.

At the core of the platform sits DUX 2.0, Loop's next-generation domain-specific language model engineered to extract and normalize data from PDFs, emails, spreadsheets, and enterprise resource planning systems. The engine collects over 200 data points per shipment and expands into customs, tariff document review, and purchase order matching.

CEO and co-founder Matt McKinney traces the problem back to his days at Uber Freight, where invoice reconciliation issues revealed an industry-wide data quality crisis. "When we were on the freight product at Uber Freight, we found an industry-wide problem: invoice reconciliation," McKinney said. "As we got into that and realized what was causing all the issues and exceptions, we discovered it was bad data."

McKinney anticipated large language models would reach maturity around 2030, but the technology delivered in 2025, making Loop's mission possible. The breakthrough arrived faster than anyone expected (a problem that has plagued users for years, frankly).

The platform's Exception Agent operates as an autonomous AI teammate that manages touchless exception handling — initiating disputes with carriers, resolving payment queries, and ensuring accurate invoice payments. McKinney described how "swarm agents" work together, with process validation agents checking each other's work to maintain accuracy.

One large retailer had 200 customer packages inadvertently delivered to pickup locations instead of residences. An agent detected this in hours and immediately notified the carrier. The next day, the carrier picked up those packages and delivered them to customers' homes on time. Previously, resolution would have required customer complaints filtering through care teams, manual investigation, and weeks of back-and-forth.

Loop Intelligence transforms company data into strategic power through an AI Assistant that accepts natural-language prompts for network optimization and financial clarity. McKinney positioned the platform as a layer that orchestrates across existing execution systems. "Think of those as execution systems that are really databases, and they are a tool that humans use," McKinney said. "Agents actually do all the work. So those are the system of records. Agents are the system of action."

The $95 million investment arrives as supply chains face volatile operating conditions — rising energy costs, tariffs, and nearshoring pressures reversing 50 years of globalization trends. According to Loop's official announcement, the company plans to use the capital to hire AI engineers, expand into trade compliance, warehouse, procurement, and inbound logistics data.

Loop works with leading brands including Outset Medical, Clemens Food Group, Olipop, Kendra Scott, and Dot Foods. The company aims to become the foundational platform for logistics and supply chain decision-making, helping businesses turn fragmented operational data into action across a comprehensive range of operational and financial decisions.

Antonio Gracias, founder, CEO, and chief investment officer of Valor, noted that Loop went deep into one of the hardest parts of the supply chain and turned it into an advantage for their customers. "That foundation extends into other operational and financial functions, which is why Loop is positioned to become the intelligence layer of the entire supply chain."

The platform's ability to integrate disparate data sources positions it as a critical tool for companies looking to modernize their supply chain infrastructure without replacing existing systems. This matters because most enterprises cannot afford to rip out their ERPs, TMS, WMS, and order-management platforms.

Supply chain operations are increasingly complex, with data scattered across legacy systems. This fragmentation creates significant challenges for supply chain leaders attempting to measure cost-to-serve, optimize logistics networks, or respond quickly to market disruptions. Loop's approach focuses on back-office supply chain operations first, targeting areas where data fragmentation often has the highest economic impact.

Whether enterprises actually pay for this level of integration remains the real question. The technology exists. The funding is secured. But convincing logistics teams to trust autonomous agents with their most critical operations is a different challenge entirely.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn