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Dispatcher copilot across 200+ daily routes

A regional 3PL ran 200+ daily routes through two dispatchers and a whiteboard. A copilot on McLeod and Samsara cut planning from 2 to 3 hours to under 1 hour.

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Summary

Client
Logistics Agency
Location
Details to follow
Modules
Dispatcher copilot, SMS agent, Email agent
Integrations
McLeod, Samsara
Timeline
6 weeks to first deploy
Commitment and result
Agreed in the build plan

Client anonymized until they approve naming.

A regional logistics agency (LTL and FTL, 40+ shippers) dispatched everything through two senior dispatchers and a whiteboard. We built a dispatcher copilot on McLeod and Samsara drafting the next-day board, then automated check-calls by SMS and inbound shipper email. Route planning per dispatcher fell from 2 to 3 hours to under 1; shipper NPS rose from 32 to 51.

What was the business?

A regional logistics agency operating across the Eastern US, managing LTL and FTL freight for 40+ shippers. $15M to $25M in annual revenue, a fleet of 80+ owner-operators and company drivers, dispatched from a single operations center.

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 by hand on a physical whiteboard and transferred to McLeod each morning. It took 2 to 3 hours per dispatcher per day. In peak season they regularly worked until 8 or 9 p.m. to build the next day's board.

Check-calls were entirely manual. Each dispatcher made 60 to 80 calls a day to drivers and shippers for status updates, time that could have gone to load optimization.

What we built

Dispatcher copilot. An AI dispatch assistant integrated with McLeod and Samsara. It suggests driver-to-load assignments from location, hours available, equipment type, and shipper preferences, and generates the next-day board as a draft for dispatcher review and approval.

SMS agent (check-call automation). Pulls real-time location from Samsara, checks it against pickup and delivery windows, and sends proactive updates to shippers. Exceptions (late, off-route, breakdown) escalate to the dispatchers.

Email agent. Handles inbound shipper email: rate requests, load tenders, POD requests, and status inquiries. Routine requests are handled on their own; complicated ones go to the right person.

Media pending

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Media pending

Details to follow

How it went live

  1. Weeks 1 to 4: deep diagnosis of the dispatch workflow, a McLeod data audit, and driver interview sessions.
  2. Weeks 5 to 6: dispatcher copilot MVP deployed in suggestion mode only; dispatchers approve every assignment.
  3. Weeks 7 to 10: check-call SMS automation deployed and the feedback loop established.
  4. Weeks 11 to 16: email agent deployed; copilot moved to semi-autonomous mode.
  5. Month 5 onward: ongoing optimization, expanded to include rate quoting assistance.

A custom implementation spanning 6 months: dispatcher copilot, SMS agent, email agent, and custom integration work. It moved to ongoing support at a flat monthly rate.

The numbers

  • Route planning time per dispatcher

    Before
    2–3 hours
    After
    under 1 hour (a 50% reduction)
    When
    Mar 2026
    Method
    Route planning time dropped from 2–3 hours to under 1 hour per dispatcher.

  • Daily routes managed

    Before
    not stated
    After
    200+
    When
    Mar 2026
    Method
    Details to follow

  • Manual check-calls per day

    Before
    60–80
    After
    10–15 exception-only calls
    When
    Mar 2026
    Method
    Check-calls dropped from 60–80 manual calls/day to 10–15 exception-only calls.

  • Shipper satisfaction (NPS)

    Before
    32
    After
    51
    When
    Mar 2026
    Method
    Shipper satisfaction scores (measured via NPS) increased from 32 to 51 in the first quarter post-deployment.

  • Dispatcher end of day

    Before
    8–9 PM
    After
    5–5:30 PM
    When
    Mar 2026
    Method
    Dispatchers went from working until 8–9 PM to finishing by 5–5:30 PM.

In the client's words

To follow with the customer's permission

What would we 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 logistics engagements since, we capture driver and shipper preferences as structured data during the diagnosis phase.

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