Loop’s AI Logistics Data Platform Gets US$95m Funding

Loop is advancing its AI platform capabilities following a significant US$95m Series C funding round, marking a major milestone in the development of full-stack AI for efficient logistics operation.

The logistics data platform is leveraging this investment to expand its machine-learning infrastructure and technical talent acquisition, positioning itself at the forefront of AI-driven data unification technology.

As organisations grapple with increasingly fragmented data environments, Loop’s AI systems demonstrate how purpose-built machine-learning models can transform disparate information streams into cohesive, actionable intelligence.

Building AI on fragmented data

Loop operates as a full-stack AI platform designed to tackle one of enterprise technology’s most persistent challenges: data fragmentation across logistics and supply chain systems.

The platform’s architecture unifies shipment and tracking data that typically exists in isolated silos, creating a centralised intelligence layer that can process and contextualise information from multiple sources simultaneously.

The Series C round was led by Valor Equity Partners and the Valor Atreides AI Fund, with participation from 8VC, Founders Fund, Index Ventures, J.P. Morgan Growth Equity Partners and Tao Capital Partners.

This capital injection enables Loop to scale its platform across additional enterprise use cases whilst investing heavily in both emerging and established AI talent to strengthen its product and engineering capabilities.

The technical challenge Loop addresses stems from the reality that organisational data exists across legacy systems with inconsistent formats and limited interoperability.

These fragmented data environments create significant barriers to effective AI deployment, as machine-learning models require consistent, high-quality data inputs to generate reliable outputs.

“We see every day how much pressure companies are under to manage supply chains through constant disruption and how often critical decisions are still being made on top of fragmented data and brittle systems,” says Matt McKinney, Loop CEO and Co-founder.

“This investment lets us expand our platform and connect the financial and operational data that our customers need to make better decisions, faster.”

Vertical AI architecture

Loop’s technical approach centres on what it describes as verticalised AI, focusing initially on back-office operations where data fragmentation typically carries the highest computational and operational complexity.

Rather than attempting to build generalised AI systems, Loop's machine-learning models are specifically trained on logistics and supply chain data patterns, enabling more accurate interpretation and processing of domain-specific information.

This specialisation could prove crucial as AI deployment across enterprise systems continues to face challenges related to data quality and consistency.

Loop’s platform processes data across multiple system types, including Enterprise Resource Planning (ERP), Transportation Management System (TMS), Warehouse Management System (WMS) and order-management systems.

By creating connections across these traditionally siloed platforms, the AI infrastructure enables real-time data synthesis that wasn't previously possible with conventional integration approaches.

The system transforms trapped operational data into an intelligent framework that can be accessed and analysed across different organisational functions.

Antonio Gracias, Founder, CEO and Chief Investment Officer of Valor, explains: “Loop went deep into one of the hardest parts of the supply chain and turned it into an advantage for their customers.

“Through the AI systems they’ve built, they’re taking data that was previously fragmented and inaccessible and are turning it into intelligence that improves cost, processes and working capital.”

Introducing DUX intelligence models

The technical foundation of Loop's platform is DUX – a family of specialised models and agents engineered to address complex logistics and supply chain challenges.

This AI system combines three distinct capabilities: data processing, document understanding and domain expertise.

By integrating these elements, DUX can contextualise information across disparate systems whilst maintaining awareness of industry-specific requirements and constraints.

According to Loop, DUX provides customers with a unified analytical view by processing information through machine-learning models trained specifically on logistics data patterns.

The platform is currently deployed with brands including Olipop, Outset Medical and Dot Foods, where it processes operational data to generate insights and enable automation frameworks.

These implementations serve as test cases for Loop’s AI architecture, validating its approach to handling real-world data complexity at enterprise scale.

The new funding will support Loop's expansion into additional use cases across logistics and supply chain operations, whilst enabling the company to grow its technical team.

The platform aims to establish itself as foundational AI infrastructure for transforming fragmented enterprise data into structured intelligence systems, particularly for organisations operating across complex, multi-system environments.