Logistics has long depended on tribal knowledge, the unwritten rules and hard-earned judgment held by a small group of experienced operators. They know which carriers actually perform on certain lanes, how specific customers expect exceptions to be handled, and what worked the last time a shipment went wrong. That knowledge has powered freight networks for decades, quietly passed along through emails, spreadsheets, Slack messages, and memory.
But as logistics organizations scale and automate, that informal model has become a real operational risk.
“Logistics teams don’t lack information,” said Harish Abbott, co-founder and CEO of Augment. “They lack shared context, delivered at the moment decisions need to be made.”
That insight drove the launch of Augment’s Knowledge Hub, a freight-native knowledge layer designed to capture how logistics companies actually run their operations and surface that intelligence directly inside daily workflows. Rather than functioning as a traditional knowledge base, Knowledge Hub acts as infrastructure, unifying operational data, policies, historical decisions, and institutional judgment into a governed system that supports execution in real time.
The product grew out of Augie, Augment’s AI teammate built to take tedious, repetitive work off operators’ plates. As Augie took on more responsibility, it needed to understand nuance: SOPs that vary by customer, carrier preferences that change by lane, and escalation patterns that depend on context rather than rigid rules.
“We were encoding all of that context so Augie could do its job,” Abbott said. “Over time, we realized we were also capturing how the company thinks.”
Customers soon began asking Augie higher-order questions. Why was this carrier chosen for this lane? Is this customer becoming a churn risk? Should a rep spend time here or focus elsewhere? Answering those questions requires stitching together multiple data sources, load history, service performance, customer behavior, financials and delivering judgment, not just information.
That capability became the foundation for Knowledge Hub.
Knowledge Hub is designed to meet operators where they work. It can be embedded into TMS workflows, Slack, Microsoft Teams, email, and customer portals, delivering context at the moment of action. When a load goes sideways, operators don’t need a document repository, they need clarity about what happened before, what’s expected now, and what options are acceptable.
Exception handling, where experience matters most, has historically been the hardest challenge to systematize. Augment addresses this by acknowledging ambiguity. When guidance conflicts, decisions are escalated to process experts rather than forced into a single answer. In cases where no clear right answer exists, Knowledge Hub presents vetted options and relevant context, allowing human judgment to remain central, especially for newer operators.
As that judgment becomes shared rather than siloed, organizations begin to change. Onboarding accelerates. Senior operators field fewer repetitive questions. Customers receive more consistent answers and service levels. According to Abbott, some customers have even tied productivity gains to better incentives for frontline teams, reinforcing adoption.
Still, the biggest obstacle isn’t technology.
“AI in logistics will be limited by change management before it’s limited by capability,” Abbott said. “The hard part is building the culture and incentives so people trust and use it.” The goal, Abbott said, isn’t replacement, but amplification. “How do we turn people into 10x operators?” he said. “That starts by making institutional knowledge work for everyone, all the time.”
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