ShipBob, a leading tech-enabled third-party logistics powerhouse serving thousands of brands globally, was faced with a knowledge management challenge common to fast-growing companies.

With over 1,300 globally distributed employees, critical institutional knowledge was trapped in organizational silos and difficult to access for critical customer-facing tasks.

As a mission-driven company with the goal to deliver best-in-class logistics solutions, ShipBob has invested heavily in AI technologies to improve internal knowledge sharing, which will increase customer satisfaction and drive overall efficiency gains.

After experimenting with multiple off-the-shelf AI solutions, ShipBob chose Contextual AI to power its knowledge management platform, which has been deployed to over 500 global employees across 14 teams, saving over 10,000 employee hours in wait time annually.

The distributed knowledge challenge

ShipBob prides itself on providing highly customizable logistics solutions to its customers. With revenues and its customer base experiencing a strong double-digit rate growth annually, ShipBob needed to rapidly scale its service teams to meet the increasing volume and complexity of customer demands in order to maintain its high bar for customer satisfaction.

One challenge to meeting the promise of being “custom at scale” was the fact that much of the critical information required to address new customer requests was held by a small number of highly tenured and geographically distributed employees. For example, answering the question of “Can I use colored tape on external packaging to match my branding?” might require consultation with experts in global fulfillment, operations, and product management.

Due to the rapid growth and international expansion of the company, institutional knowledge had accumulated organically, and there was no centralized document repository that could be referenced as a single source of truth.

"We don't have a centralized wiki. It can be quite difficult to find the right information if you don’t already know where it lives." explained Natasha Gangal, AI Strategy Lead. “At ShipBob, it is very difficult to find the right source of information without relying on other people, and this can become a bottleneck when decisions have to be made quickly."

As a consequence, new customer service employees often relied on direct lines of communication with internal experts for onboarding and customer issue resolution. The process of identifying and waiting for responses from subject matter experts often caused multi-day delays, resulting in thousands of hours of cumulative customer impact each year. To make matters worse, the same experts were often asked similar questions repeatedly as new employees onboarded, expending valuable time that could have been otherwise focused on strategic work.

The ShipBob Knowledge Base, powered by Contextual AI

Contextual AI partnered with ShipBob to deliver a highly specialized knowledge management platform that provides highly accurate, specific, and auditable results while integrating natively with their Microsoft Teams chat experience.

The ShipBob Knowledge Base accurately and succinctly answers user queries while providing detailed attributions and links to the underlying source materials. Importantly, the solution includes a powerful feedback module that automatically notifies subject matter experts when encountering a query with no reliable source information. This combination of accuracy and feedback has created a flywheel effect where increasing adoption and documentation improvements continue to create a more powerful and useful experience over time.

With Contextual AI, ShipBob has turned fragmented knowledge into a strategic advantage. Teams can now deliver faster, more consistent service while continuously improving their operations.

Looking forward, ShipBob plans to expand the platform beyond internal knowledge management. Insights from query patterns are already shaping product development priorities by highlighting the questions customers and employees ask most often.