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Loop Raises $95M to Tackle Supply Chain AI Fragmentation

Supply chain AI vendor Loop has raised $95 million in Series C funding, according to www.dcvelocity.com. The San Francisco firm cites persistent data fragmentation — inconsistent, inaccessible data across ERP, TMS, WMS, and other disconnected systems — as a core barrier to AI deployment. CEO Matt McKinney emphasized pressure from tariffs, supplier diversification, and energy costs. Investors include Valor Equity Partners, Founders Fund, and J.P. Morgan Growth Equity Partners. The funding will expand Loop’s platform across supplier, trade, warehouse, and procurement use cases while strengthening system integrations.

Original source: dcvelocity.com

Loop Raises $95M to Tackle Supply Chain AI Fragmentation
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Supply chain AI vendor Loop has raised $95 million in Series C funding, according to www.dcvelocity.com. The San Francisco firm cites persistent data fragmentation — inconsistent, inaccessible data across ERP, TMS, WMS, and other disconnected systems — as a core barrier to AI deployment. CEO Matt McKinney emphasized pressure from tariffs, supplier diversification, and energy costs. Investors include Valor Equity Partners, Founders Fund, and J.P. Morgan Growth Equity Partners. The funding will expand Loop’s platform across supplier, trade, warehouse, and procurement use cases while strengthening system integrations.

According to www.dcvelocity.com, supply chain artificial intelligence (AI) provider Loop has raised $95 million in a Series C funding round to expand its platform across enterprise use cases, deepen product and engineering capabilities, and invest in AI talent.

Why Supply Chain AI Deployment Remains Challenging

The San Francisco–based firm states that supply chains are among the hardest environments for AI deployment because underlying data is inconsistent, inaccessible, and spread across disconnected enterprise systems. According to the report, many businesses lack a single source of truth across operations — a barrier exacerbated by legacy systems, trapped data, and limited operational and financial visibility. Tariffs, supplier diversification needs, and higher energy costs further compound these pressures in today’s volatile operating environment.

Platform Expansion and System Integration

Loop says its platform addresses these challenges by expanding across new data types and use cases—including supplier, trade and compliance, warehouse, procurement, and inbound logistics data—while strengthening connections across ERP, TMS, WMS, and order-management systems. The company emphasizes its ability to unify financial and operational data to support faster, more informed decision-making.

“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.” — Matt McKinney, CEO and Co-founder of Loop

Funding Leadership and Investor Participation

The Series C round was led by Valor Equity Partners and the Valor Atreides AI Fund. Participating investors include

  • 8VC
  • Founders Fund
  • Index Ventures
  • J.P. Morgan Growth Equity Partners
  • Tao Capital Partners

Broader Industry Context

This investment arrives amid growing recognition of AI’s uneven adoption across supply chains. A concurrent report cited by DC Velocity — WBR Insights’ LogiPharma Playbook: 2026 Supply Chain & Logistics Insights — found that 65% of pharma supply chain leaders have limited confidence in AI’s ability to predict or mitigate disruption. While AI adoption is highest in demand planning (59%), inventory optimization (57%), and logistics orchestration (49%), the industry remains in transition from experimental use to enterprise-wide impact. That aligns with Loop’s stated mission: moving customers from reactive, siloed workflows toward proactive, integrated decision support. For global supply chain professionals, this signals increasing vendor focus on interoperability, data unification, and cross-system orchestration — not just point-AI tools.

Source: DC Velocity

Compiled from international media by the SCI.AI editorial team.

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