project44 Turns Supply Chain Data Into AI-Powered Decisions with Snowflake

AI Analyst Mo combines project44's logistics intelligence and network data with Snowflake Cortex AI to deliver consistent, data-grounded answers to complex supply chain questions

CHICAGO, Nov. 10, 2026 (GLOBE NEWSWIRE) -- project44, the Decision Intelligence Platform for the modern supply chain, today announced an integration with Snowflake, the AI Data Cloud company, to advance reliable AI-powered supply chain decision-making through Mo, project44's AI supply chain analyst.

In practice, teams work with Mo by asking questions in plain language and getting answers and recommended next steps. At its core, Mo is an AI agent harness: the guardrails, workflows, and execution capability built around the language model that let AI take action on supply chain tasks, not just respond to prompts.

The integration pairs project44’s logistics ontology, semantics, and network data, approximately 1 trillion shipment records over the last decade, with Snowflake Cortex AI. This approach helps supply chain teams move from natural-language questions to answers grounded in their own operational data and business definitions.

For AI to execute consequential business decisions, it must do more than generate a plausible response. It must interpret the question correctly, apply the right business definitions, retrieve the relevant data, and deliver an accurate and consistent answer quickly enough to inform action. This is particularly important in logistics, where straightforward terms can mean different things to different companies. On-time performance, for example, may depend on customer-specific definitions, contractual requirements, shipment modes, and the milestones used to measure transit time.

Mo uses project44’s world’s largest, most accurate, real-time logistics data graph and customer context to interpret these questions, combining that interpretation with structured data retrieval to make AI-generated supply chain insights more consistent, traceable to underlying data, and useful for operational decisions.

project44 customers are already realizing measurable value from the data and context Mo is built on. In detention and demurrage alone individual enterprise customers are seeing results like a $6 million reduction in D&D exposure. In working capital, individual customers have found they can free up, $200K with a single question to Mo. In disruption management, faster access to the right signal has helped customers avoid costs tied to exceptions and delays, with results like nearly $750K in savings tied to a single initiative. Mo scales this impact across every customer and every question, turning what used to take a dedicated analysis into something any team member can act on directly.

What project44 brings

Mo's reliability rests on two things:

A unified data model and logistics ontology: For a decade, project44 has defined shipment, carrier, contract, and milestone data consistently across its network, and the logistics ontology gives that data a shared structure and meaning. Mo draws on approximately 1 trillion shipment records, plus real-time milestones and predictive ETAs.

Semantics and customer-specific requirements: On top of that foundation, Mo applies project44’s logistics semantics, which define how concepts like shipment, delay, and on-time performance are interpreted, along with each customer’s own business rules, transportation modes, contract terms, and exceptions, to understand what a question actually means for that customer.

That second layer is what makes the answers usable. Without customer-specific definitions, a general-purpose model may misinterpret terms such as “underperforming” or “your lanes.” Definitions of on-time, in-full performance can vary across shippers, and some measure transit time from cargo ready date while others measure from pickup. Mo uses those definitions to interpret questions in the customer’s business context so the same question gets the same answer every time it’s asked.

“Reliable data, reliable signal, reliable action. That’s the sequence, and project44 provides it with our logistics ontology, customer contracts, a decade of network data, and real-time milestones. Cortex AI retrieves the figures from the data. Together, that’s what turns a capable model into something operations teams can trust with real decisions,” said Jett McCandless, founder and CEO, project44.

What Snowflake brings

Mo uses Cortex Analyst to translate its interpreted questions into SQL and retrieve results directly from project44's data in Snowflake. This approach grounds answers in structured data, rather than relying on a language model to reproduce figures from memory or inference.

Together, project44's logistics intelligence and reliability harness and Cortex AI's data retrieval capabilities help Mo move from a plain-language question to a data-grounded answer. Each component has a distinct role: project44 provides the logistics context needed to interpret the question, while Cortex AI supports retrieval of the underlying data.

“The opportunity for AI in supply chains is to turn operational data into better-informed decisions about where to focus attention and resources. By combining project44’s logistics expertise with Cortex AI, Mo helps teams ask questions in business terms and use the answers to respond to disruptions and identify opportunities to reduce costs,” said Will Allen, Head of Snowflake CoCo and CoWork, Snowflake.

What the combination delivers

Reliable AI requires accuracy, consistency, and speed. In internal testing, with both systems working from the same underlying data, Mo beat general-purpose large language model on each. On accuracy, Mo answered 10 of 10 verified test questions correctly while the general-purpose large language model answered 2 of 10 correctly. On consistency, Mo gave the same answer all 10 times while the LLM did so 2 of 10 times. On speed, Mo responded 2.5 times faster because it can immediately pinpoint the data for customers Mo’s harness, not the model, drives these results.

210 customers now use Mo, which has answered over 50,000 questions to date.

“Supply chain decisions move fast, and the teams making them need answers that are just as fast. Mo helps us get there, putting accurate, data-grounded answers directly in the hands of the people who need them, without the back-and-forth of pulling reports or waiting on analysis,” said Doug Cantriel, Head of North America Transportation at Ford Motor Company.

Mo is part of project44’s Decision Intelligence Platform and is available now for project44 customers. To learn more or request a demo, visit project44.com/ai-agent-orchestration/mo/.

About project44

project44 is the Decision Intelligence Platform for the modern supply chain. Its context-based AI transforms fragmented logistics management into unified intelligence, bringing certainty to global supply chain operations. With intelligent transportation management, end-to-end visibility, yard management and last mile solutions, and a preferred carrier network of 285,000 carriers, project44 connects over 1.5 billion shipments annually for over 1,000 leading brands in manufacturing, automotive, retail, life sciences, food and beverage, CPG, and oil, chemical and gas. Learn more at project44.com.

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