Mo reasons across each customer’s own data and business rules, drawing on the context project44 provides through the world’s largest, most accurate, real-time logistics data graph to answer questions teams can act on immediately.
CHICAGO, July 23, 2026 — project44, the Decision Intelligence Platform for the modern supply chain, announced Mo, a conversational AI supply chain analyst built directly into the project44 platform. Mo reasons across a customer’s own data and business rules, drawing on the world’s largest, most accurate, real-time logistics data graph, so the answers operators get are more reliable, grounded in the data feeding them.
Supply chain teams have more data than ever, but they can’t get fast, reliable answers without manually pulling reports, navigating dashboards, or exporting data into a separate system. Most of the solutions that exist today, whether generic AI or traditional analytics platforms, are generalized to work the same way across every customer, so getting to a specific answer means building a one-off workaround for every question. That process delays decisions past the window where action still matters, and it bottlenecks through analyst experts, so most of the team can’t self-serve an answer. Every extra step slows the decision down and chips away at trust in the answer itself.
Mo’s answers are grounded in a customer’s own shipment data, business rules, and carrier history, not generic industry benchmarks or a vanilla LLM’s training data. Tuned with a semantic understanding of the language and relationships that govern supply chain data, Mo reasons across a customer’s SOPs, strategies, and KPIs alongside the world’s largest, most accurate, real-time logistics data graph, to accurately understand the minutiae of a specific operations.
Compared to a human-led process, Mo turns a task that used to take hours or days of manual analysis into an answer in minutes. Compared to a Claude Opus, Mo delivers more accurate answers, with lower latency and more consistent results. In internal testing, Mo answered operational questions using a customer’s own real-time data in about 2.5 minutes, versus roughly 18 minutes for a Claude Opus after manually exporting data, filtering rows, and building context by hand. On accuracy, Claude OpusFS answers were wrong or overstated on most claims tested, including a late-rate calculation off by nearly 18 points and a claim that 40% of shipments were untracked when the actual figure was 0.8%. Mo needed none of the manual setup and returned accurate answers consistently.
That grounding is what makes Mo reason rather than retrieve: it builds context over time from a customer’s shipment history, weighs multiple variables instead of returning a single number, and signals when its confidence is low instead of guessing. The result is a reasoning architecture, not a chatbot wrapper, that’s materially more accurate than Claude Opus, and one that puts supply chain insight in reach of a customer’s whole team, not just their power users.
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 $600K $1.8 million 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.
At launch, Mo can query into Truckload (FTL), Less-than-Truckload (LTL), and Ocean Freight data with additional functionality being added through the rest of 2026.
Mo is also a step toward a broader vision at project44: giving customers the inputs to build their own models on top of project44’s semantics and context, what we call sovereign models. Rather than relying on a single, one-size-fits-all AI, customers will be able to shape models grounded in their own data and business rules, with project44’s data graph as the foundation.
“We’ve spent more than a decade building the world’s largest, most accurate, real-time logistics data graph. But connecting the data, cleaning it, and building rules to govern it was just the beginning. Mo takes all of this context and customer data and makes it simple and accessible, solving hours-long tasks in seconds. Multiply that across every question a supply chain team asks in a day and, suddenly, teams are going to see a fundamental transformation in how they work on a daily basis,” said Jett McCandless, founder and CEO of project44.
Mo is already delivering on its early promise. Ford, one of Mo’s early access customers, has been using it to get answers faster, without waiting for reports or analysis from other teams first.
“Supply chain decisions move fast, and the teams making them need answers that are just as fast. Mo will help 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.
To learn more or request a demo, visit www.project44.com/ai-agent-orchestration/mo/