Concept buildA worked example of how we would build this for a typical business. Not client work: no client, no client data, no achieved results.
AI Voice Agents
Concept buildA concept build for an outbound voice agent that makes routine ETA check-in calls to truck drivers and logs each answer in the fleet’s TMS. It calls only when the truck is parked or at a check-in window the driver chose, and it alerts a dispatcher straight away when a driver reports a breakdown or a delay.
The brief
A typical regional fleet in the US runs on check-in calls. Dispatchers phone drivers several times a day to confirm ETAs, collect delay reasons and pass updates to customers, then type the answers into the transportation management system (TMS) between other calls. On a busy day those calls crowd out the work that needs a dispatcher’s judgement: rerouting around a breakdown, rebooking a missed appointment or helping a driver with a problem. The answers end up as free-text notes, so nobody can see late-running loads at a glance. Phoning drivers who are on the road also raises a safety question the fleet would rather not manage one call at a time.
Explainer
Explainer · 7 slides · about 42 s
Concept buildConcept build: Driver Check-in Voice Agent for a Regional Fleet
Concept build, explained in 7 slides: a concept build for an outbound voice agent that makes routine ETA check-in calls to truck drivers and logs each answer in the fleet’s TMS.
Slide 1 of 7
01 · AI voice agents
Driver Check-in Voice Agent for a Regional Fleet
Concept build: Driver Check-in Voice Agent for a Regional Fleet.
02 · The problem
What a typical regional trucking fleet is up against
A typical regional fleet in the US runs on check-in calls.
03 · The approach
How we would build it, in 5 steps
We would build it in 5 steps, from call audit and the question set to pilot on a few lanes.
04 · Architecture
How the pieces connect
How the parts connect, from the first input to the person who reviews the result.
05 · Demo · sample run
One routine check-in and one breakdown
A sample run, step by step, including where a person steps in.
06 · Typical targets
What we would measure it against
Typical targets, each with the basis it would be measured against.
07 · Next step
Scope a build like this for your business
Book a strategy call to scope a build like this.
Architecture
Architecture of the Driver Check-in Voice Agent for a Regional Fleet concept: Check-in schedule → Outbound calls (due check-in); Telematics via API → Outbound calls (truck parked); Outbound calls → Speech in and out (call); Speech in and out → Extraction model (transcript); Extraction model → Dispatcher (breakdown alert); Extraction model → TMS via its API (ETA + status); Extraction model → Call records (structured record).
Diagram with 8 parts: Check-in schedule (source), Telematics via API (source), Outbound calls (processing), Speech in and out (processing), Extraction model (model), Dispatcher (human), TMS via its API (system of record), Call records (system of record).
Connections:
Sample run
Truck parked at a fuel stopRoutine
Telematics shows the truck stationary; a check-in for sample load 4471 is due.
Automated check-in callRoutine
The agent says it is an automated check-in; the driver confirms the next stop.
ETA captured and read backRoutine
The driver gives a 16:30 arrival; the agent reads it back and the driver confirms.
TMS updatedRoutine
Status, location and ETA written through the API, with the transcript linked.
Next driver reports a breakdownStopped
Issue word matched; the agent checks everyone is safe and gets the location.
Dispatcher alertedA person takes over
SMS to the on-duty dispatcher; the load is flagged with the call summary.
Build plan
We review a week of dispatcher check-in calls and notes to separate routine ETA confirmations from calls that need a person. The result is a short question set for routine check-ins: current location or next stop, updated ETA and any issue. Everything outside that set, from detention disputes to personal matters, stays with dispatchers.
Drivers opt in to automated check-in calls during onboarding, with wording that says an AI voice will call, because the FCC treats AI-generated voices as artificial voices under the TCPA. The agent calls only when telematics shows the truck parked or at a window the driver picked, since US rules bar commercial drivers from holding a phone while driving. Drivers can switch to text check-ins at any time.
Calls go out through Twilio with streaming speech-to-text and a natural voice in English or Spanish. A language model turns the driver’s answer into a fixed record: load, location, ETA, issue type and a confidence score. A low-confidence answer gets one clarifying question; if it is still unclear, the record is marked for a dispatcher instead of being guessed.
Clean records update the load in the TMS through its API, with the call transcript linked. Issue words such as breakdown, accident or a missed appointment trigger an SMS alert to the on-duty dispatcher and flag the load, so the person who can fix the problem hears about it as the call ends, not at the next check-in round. A driver who reports an injury is told to call 911 first.
The pilot runs on a handful of lanes with drivers who volunteer, and dispatchers listen to a sample of calls each day. We compare the agent’s records with what drivers actually said, adjust the question wording, and add lanes one at a time only while record accuracy holds.
Typical targets
Typical target
50–70% of routine ETA check-ins handled without a dispatcher
Basis: The share of routine ETA calls in the one-week call audit, confirmed against pilot call volumes
Typical target
Every check-in call ends as a structured TMS record or a dispatcher flag
Basis: Design property: each call writes load, location, ETA and issue fields, or flags the load
Typical target
Breakdown and delay reports reach a dispatcher as the call ends
Basis: Design property: issue words trigger an SMS alert and a load flag; delivery time is measured in the pilot
Typical target
No check-in call placed to a moving truck
Basis: Design property: calls wait for telematics to show the truck parked, or for the driver’s chosen window
Your project
The opening line, recording notice and opt-out wording checked for the places your callers are.
Numbers, call routing and the fallback to your staff set up under your accounts, with after-hours rules you choose.
Transcripts sampled and reviewed with your team, and every misheard phrase added to the test set.
Latency, accents and noisy-line behaviour checked on your numbers before callers reach the agent.
Where this fits
FAQ
No fleet has. This is a concept build for a typical US regional fleet, and the loads, drivers and calls in the demo are made up. The 50–70% range depends entirely on how many of a fleet’s own check-in calls turn out to be routine, so the first week of any real project is spent counting them.
Yes, and the plan starts there. Drivers give consent during onboarding with wording that says an automated voice will call, the fleet’s counsel approves that wording, and every call opens by saying it is an automated check-in. A driver can ask for a person or for no more automated calls, and that choice is recorded straight away.
It checks before dialing. When the fleet’s telematics shows the truck moving, the call waits for the next stop or the driver’s chosen check-in window, and if neither comes in time it sends a text instead. Fleets without telematics data use fixed windows agreed with each driver.
The agent asks one short clarifying question, then reads the ETA back for confirmation. If it still cannot extract a confident answer it does not guess: the load is flagged for a dispatcher with the transcript attached. Calls recorded in noisy cabs with the drivers’ permission are a standard part of the test set before launch.
An API that lets an approved service account update load status and add notes. During scoping we check which fields the API can write and how it handles a dispatcher editing the same load at the same moment. If a system has no usable API, the agent writes to a review queue that dispatchers apply instead.
Worked examples
Concept builds are worked examples, not client work.
Concept buildAI Voice Agents
A concept build for a voice agent that answers a dental clinic’s phone after hours in Arabic and English and books routine visits into the clinic calendar. It sends an SMS confirmation, moves callers who describe urgent symptoms straight to the on-call dentist, and never gives clinical advice.
Next step
Book a free 30-minute strategy call. Bring one process you want to automate and we will talk through scope, integrations and a sensible first step.
Free 30-minute call · no commitment
