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Loop
Loop is the industry’s first Logistics Data Platform. Using full-stack AI, it unifies messy shipment and tracking data so logistics teams can operate with...
Loop
Loop is the industry’s first Logistics Data Platform. Using full-stack AI, it unifies messy shipment and tracking data so logistics teams can operate with clarity and efficiency. The company works to eliminate legacy back-office processes that drain profits in transportation and supply chain, providing freight audit, payment, visibility, and spend-control capabilities.
General information
Firm type
Asset Manager
Year founded
2009
Location
Region
North America
Country
United States
City
Miami
Corporate office
Dallas, Doylestown, United States
Principals
Matt McKinney
Co-Founder and CEO
Shaosu Liu
Co-Founder and CTO
Sector focus
Frequently asked questions
How does Loop's platform differ from a standard freight audit and payment provider?
Loop operates a full-stack logistics data platform rather than a traditional outsourced audit service. Its AI normalizes fragmented shipment and tracking data into a single source of truth, then layers audit, payment, and cost-allocation workflows directly onto that foundation. This architecture allows clients to run continuous contract-enforcement analytics and dynamic spend reallocation from the same system, rather than receiving periodic audit reports from a third party and executing changes manually.
What types of enterprises use Loop, and what ROI have they reported?
Loop's confirmed clients include industrial manufacturers like Gillig, freight brokers such as Loadsmart, and Fortune 500 shippers in the pharmaceutical sector. Gillig reported 6% transportation savings identified through Loop's analytics (per the firm, undated). Loadsmart reduced its invoice-to-approval cycle to 13 minutes per shipment. A pharmaceutical client identified $2.4 million in quarterly savings through a scenario-planning exercise that shifted volume between FedEx and UPS.
Which carriers and modes does Loop's platform support?
Loop's platform demonstrates integration with major parcel carriers, including FedEx and UPS, based on disclosed client case studies involving volume-shifting and cost-to-serve modeling. The platform's data-normalization layer ingests shipment data from multiple carrier formats, and its analytics cover parcel-shipment visibility, carrier accountability, and contractual compliance across transportation modes. Specific less-than-truckload, full-truckload, or ocean carrier coverage beyond parcel is not publicly disclosed.
How does Loop's AI normalize supply chain data, and what makes its approach different?
Loop applies AI at the data-normalization layer, meaning the platform learns carrier-contract structures, invoice formats, and cost anomalies during ingestion rather than applying analytics post hoc to a cleaned dataset. This full-stack approach means optimization logic and audit rules run on the same data foundation, so cost findings are immediately actionable in payment workflows. The platform was recognized by Gartner in its 2026 Market Guide specifically for its AI-driven data foundation and combined audit, payment, and visibility services.
Profile maintained by Altss using OSINT (open-source intelligence), regulatory filings, licensed data partners, and verified direct submissions. Read the methodology. Last updated: . Continuous refresh with full update cycles at least every 30 days.
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