Case study · Application

Zonik AI is a logistics product we co-founded.

n8n to custom appAI voice agentslogisticsops automation

From n8n load tracking to a custom app that calls the driver when data goes dark

We shipped a Dispatcher + Worker loop on n8n, then transferred the full system into a custom application: self-scheduling load checks, VAPI voice check-ins, and a human exception queue when the automation cannot close the issue.

Client

Zonik AI

Solution

Custom app (from n8n)

Time to launch

May 2026 → ongoing

With first users: Beta
Tightest check interval: 5 min
Human exceptions: 1 queue

Overview

n8n to find the contract, then a custom app to run it

Check intervals, risk rules, and call holds started as n8n knobs - easy to tune and replay. When volume and the need for an owned runtime outgrew the canvas, we transferred the full loop into a custom application with the same contract.

Challenge

Load tracking never sleeps

Someone has to watch GPS, spot late ETAs, and call drivers - including at 3am. Fleets add night shifts or stretch dispatchers too thin. Both scale headcount with the fleet.

Our side

Dispatcher / Worker architecture
n8n prototype + call result contract
Full transfer from n8n into a custom application
Monitoring and on-call for the system

Partner side

Partner-led sales and operator relationships
Fleet driver, truck, and load data

Solution

Dispatcher + Worker, self-scheduling each load

The Dispatcher wakes on a schedule and only pulls loads due for a check. The Worker evaluates one load - GPS, ETA, severity - then writes its own next check time. Quiet loads go quiet. At-risk loads get tight intervals and, when needed, an AI voice call via VAPI.

Features

Self-scheduling triage
Each load carries its next-check time. Cost follows risk, not fleet size.
AI voice check-ins (VAPI)
When data cannot explain a delay, the system puts a voice agent on the phone and gets a structured reason back.
Call hold + force-call
No repeat calls after a clean answer. Dispatchers can force contact when they need it now.
Exception queue
Unresolved loads flip to needs-dispatcher. Humans work the queue - not every truck on the board.

Before → after

Before

Manual ELD checks, thin night coverage, problems found late. More trucks meant more monitoring hours.

After

The custom app is built to run continuously. Loads self-schedule. Drivers get called when data breaks. Dispatchers only see exceptions. n8n was how we learned the loop.

“I don't want people sitting on GPS all night.”

Nenad Stevanovic

Co-founder, Zonik AI

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Impact

What changed after launch

Beta
With first users
5 min
Tightest check interval
1 queue
Human exceptions

Built for overnight and weekend tracking without a night shift
Driver contact on the system's schedule
Structured call results - not just audio to review later
Execution cost tied to risk, not fleet size
Dispatchers work a filtered exception queue

Results

The loop now runs as a custom application in beta with Zonik's first users: triage, evaluation, VAPI contact, write-back, and escalation. It started as a full n8n Dispatcher + Worker system. n8n Cloud billed per execution; volume made the canvas the bottleneck. We transferred the whole workflow into owned code - same contract, not a JSON dump.

Technologies

CA
Custom application
N(
n8n (original orchestrator)
VA
VAPI
TW
Twilio
Xano
PO
PostgreSQL
OpenAI
SE
Sentry

If your team is doing this by phone, start on n8n - finish in an app when the canvas breaks

Watch state, contact a human when data stops making sense, extract structure, escalate the rest. We map the lightest version first. Book a free 30-minute strategy call and we will say whether that is a workflow or a product.

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“I don't want people sitting on GPS all night. It watches the loads, calls the driver if something's wrong, and we only get what it can't finish.”

Nenad Stevanovic

Co-founder, Zonik AI

Bring the bottleneck.

Book a free 30-minute strategy call. We'll map the lightest next step - whether we work together or not.