Check calls ate into dispatch capacity on every load. NAD's team was calling drivers roughly three times a day per load just to confirm status, regardless of whether anything was actually wrong.

Limited real-time visibility slowed down customer communication. Without a consistent view of where a load actually was, flagging and explaining delays to customers took longer than it needed to.

There wasn't a clear alternative in sight. NAD knew freight management could be run more efficiently but didn't have a concrete picture of what that would look like until a conversation with Chain's team clarified it.

NAD brought Chain in primarily for tracking and tracing, dispatch-to-driver communication, and GPS visibility. Rather than replacing NAD's team on every load, Chain layered automated status capture underneath their existing workflow: geofence-triggered arrival and departure events, AI-generated status updates, and driver app signals all feed into the same load record, so the team has a consistently updated picture of every load without asking for it. NAD's dispatchers stayed closely engaged with drivers and customers throughout, using Chain to remove the repetitive tracking work rather than the relationship.

AI is participating in nearly every chat-eligible load. Chain's AI took part in 95.8% of NAD's chat-eligible loads in the sample, covering the vast majority of the conversations that used to require a dispatcher to start or drive.

Automated status capture runs in the background across pickup and ETA activity. Geofence triggers, AI-generated updates, and driver app signals feed into the same load record automatically, covering the majority of origin-side and ETA activity without a dispatcher having to ask for an update.

The team stayed hands-on where it mattered. Chain handled the repetitive, schedule-based status capture in the background while NAD's dispatchers remained directly involved in customer communication and exception handling, the calls that actually require judgment.

Tracking quality itself was a pleasant surprise. Tyler pointed to the frequency of location updates and features like location spoofing detection as a genuine surprise post-launch, adding a layer of security that helps flag potential bad actors before they become a real problem.

ETA updates are almost entirely automated. Of 86 ETA update events observed in the sample, 85 were generated automatically, a 98.8% automation rate on one of the updates customers ask about most.

AI is present on nearly every load that can support it. 226 of NAD's 236 chat-eligible loads, 95.8%, had AI participation in the conversation.

AI is carrying the majority of message volume. Of 1,020 NAD-side messages in the sample, 630 were sent by AI, well over half of all outbound communication on active loads.

Pickup activity is increasingly captured automatically. Origin arrivals and departures are now regularly picked up through geofence triggers and AI-generated updates rather than a dispatcher having to confirm them by phone, though this is an area NAD and Chain are continuing to build on.

Customer service improved through faster, better-informed communication. With more consistent visibility into load status, NAD can communicate delays faster and make better decisions on the customer's behalf.

Dispatchers report meaningful time back. Tyler estimates each dispatcher is saving roughly 45 to 60 minutes a day as automated status capture replaces manual check calls, time that's going toward booking freight and supporting customers instead. "Do you want your team spending time on repetitive administrative tasks, or do you want them focused on work that actually grows your business?" Tyler asks. "Chain gives our team more time to do what they do best."