Dashboard demo · Trucking, fleet, logistics · Build in 6–10 weeks

Trucking & Fleet Operations

See an AI dispatcher predict breakdowns and re-route a fleet live.

The Trucking & Fleet Operations demo is an interactive dispatch board that shows how an AI agent layer sits on top of a fleet management system: it watches telemetry, predicts breakdowns before they happen, re-routes loads, and triggers dispatch chains automatically. Click any truck on the Texas map to see its route and maintenance status; click the AI Alert to see the auto-dispatch sequence. Tailored to Armando's NationaLease and fleet consulting work.

Texas fleet — live view

fixture · 4 trucks shown

DallasLaredoEl PasoHoustonSan AntonioLubbockAustin07142231
!

AI Alert · Truck 14

Breakdown predicted in ~2 hours. Rear-brake fault codes have trended up 3.2× over the last 6 hours. Load currently heading Dallas → Laredo.

Active jobs

Truck 07

Low

San Antonio → Houston

ETA 3h 20m·Luis M.

Truck 14

High

Dallas → Laredo

ETA 5h 45m·Sara K.

Truck 22

Low

El Paso → Odessa

ETA 2h 10m·Diego R.

Truck 31

Medium

Austin → Lubbock

ETA 6h 05m·Mike T.

Preview runs on canned data · book a call to see it on your own

What this shows

The capabilities demonstrated.

  • Live truck positions on a Texas SVG map with animated routes
  • Active jobs sidebar with ETAs and driver hours-of-service
  • AI Alert: "Breakdown predicted for Truck 14 in ~2 hours"
  • Auto-dispatch chain: alternate driver → customer notice → replacement truck
  • Maintenance risk scores per tractor with plain-English reasoning

How we'd build this for you

4 steps. Yours, not a template.

01

Integrate

We pull from your TMS (McLeod, TMW, Axon), ELD (Samsara, Motive, Geotab), and maintenance system. Data lands in a unified store with a clean schema.

02

Predict

A model over ELD signals — engine hours, fault codes, idle time, brake wear — flags tractors at high risk for a breakdown in the next 72 hours. Not black-box; every flag cites its evidence.

03

Orchestrate

An AI dispatcher proposes re-routes, backup drivers, and customer notifications. Human dispatcher approves with one click, or we configure auto-approve for low-risk actions.

04

Learn

Every accepted and rejected recommendation feeds a weekly tuning loop. The system gets measurably better at your lanes, your customers, your equipment over the first 90 days.

Stack

Tools behind this demo.

n8nAnthropic ClaudePostgreSQLSamsara/Motive APITwilio SMSRetool / custom admin

FAQ

Trucking & Fleet Operations: common questions

What TMS and ELD systems do you support?
We have integrated with McLeod, TMW, Axon, and homegrown TMS systems, and with Samsara, Motive, Geotab, and KeepTruckin on the ELD side. If your systems have an API or a nightly export, we can work with them. Integration scoping is part of week one and is priced separately only when the API is non-trivial.
How accurate is the breakdown prediction?
Honest answer: it is predictive, not prophetic. In the fleets we have worked with, the alert catches 60–80% of mechanical failures at least 12 hours early, with a false-positive rate of around 10%. Dispatchers treat it the way a pilot treats a yellow warning light — worth investigating, not panicking. It does not replace a mechanic walk-around.
Does this replace my dispatcher?
No. It augments them. The AI proposes; the dispatcher disposes. In fleets we have deployed this with, dispatchers handle 30–40% more loads per shift because the busywork (cascading a re-route, writing customer notifications, finding coverage) is drafted for them. Nobody we have worked with has reduced headcount from it.
We run 200+ trucks. Does this scale?
Yes — we have designed it for regional fleets up to around 500 tractors. Past 500, architecture decisions change (sharded data, dedicated model serving) and we scope it as a larger engagement. Sub-200 fleets typically ship in 6–10 weeks; 200–500 fleets in 10–16 weeks.

Your turn

Want this demo running on your data?

Free 30-minute discovery call. We scope the build, confirm ROI, and ship a fixed-fee quote — no surprises.

Last updated April 2026