Dispatcher copilot across 200+ daily routes
Last updated June 2, 2026
200+
daily routes managed
50%
planning time reduction
6 weeks
time to first deploy
The business
Logistics Agency
A regional logistics agency operating across the Eastern US, managing LTL and FTL freight for 40+ shippers. Running $15M–$25M in annual revenue with a fleet of 80+ owner-operators and company drivers, dispatched from a single operations center.
The problem
What Was Broken.
All dispatch ran through two senior dispatchers who had been with the company 10+ years. Every driver preference, shipper requirement, and lane rate lived in their heads. When one took vacation, the operation visibly degraded — late pickups, missed appointments, shipper complaints.
Route planning was done manually on a physical whiteboard and transferred to McLeod each morning. The process took 2–3 hours per dispatcher per day. During peak seasons, they regularly worked until 8–9 PM to build the next day’s board.
Check-calls were entirely manual. Each dispatcher made 60–80 calls per day to drivers and shippers for status updates — time that could have been spent on load optimization.
What we shipped
Production Deployments.
Dispatcher Copilot
An AI-powered dispatch assistant integrated with McLeod and Samsara. Suggests optimal driver-to-load assignments based on location, hours available, equipment type, and shipper preferences. Generates the next-day board as a draft for dispatcher review and approval.
SMS Agent (Check-Call Automation)
Automated check-call system that pulls real-time location from Samsara, cross-references against pickup/delivery windows, and sends proactive updates to shippers. Escalates exceptions (late, off-route, breakdown) to dispatchers.
Email Agent
Handles inbound shipper emails: rate requests, load tenders, POD requests, and status inquiries. Routes complex requests to the right person; handles routine requests autonomously.
How long it took
Phase by Phase.
- 1
Weeks 1–4: Deep diagnosis of dispatch workflow, McLeod data audit, driver interview sessions
- 2
Weeks 5–6: Dispatcher Copilot MVP deployed — suggestion mode only (dispatchers approve all assignments)
- 3
Weeks 7–10: Check-call SMS automation deployed, feedback loop established
- 4
Weeks 11–16: Email agent deployed, copilot moved to semi-autonomous mode
- 5
Month 5+: Ongoing optimization, expanded to include rate quoting assistance
The outcomes
What It Did.
Route planning time dropped from 2–3 hours to under 1 hour per dispatcher. Dispatchers went from working until 8–9 PM to finishing by 5–5:30 PM. Check-calls dropped from 60–80 manual calls/day to 10–15 exception-only calls. Shipper satisfaction scores (measured via NPS) increased from 32 to 51 in the first quarter post-deployment.
“Our two dispatchers used to stay until 8 PM building the next day’s board. Now they’re done by 5 and the routes are better.”
The engagement
Scope and Investment.
Started as a Custom Implementation engagement spanning 6 months — Dispatcher Copilot, SMS Agent, Email Agent, and custom integration work. Transitioned to ongoing support at a flat monthly rate.
Honest postmortem
What We'd Do Differently.
We underestimated how much institutional knowledge lived in the dispatchers’ heads about individual driver preferences. The first version of the copilot optimized purely on efficiency metrics and ignored soft factors like “Driver X doesn’t do NYC” or “Shipper Y only wants Driver Z.” We spent an extra two weeks in month 2 building a preference engine. In future logistics engagements, we now capture driver and shipper preferences as structured data during the diagnosis phase.
See If This Approach Fits Your Operation.
30 minutes. No pitch deck. We whiteboard your current stack and identify the highest-leverage automation.