Case study · Industrial

30% fewer support calls after three AI builds done in the right order

The challenge This national logistics, transportation and storage operator runs multiple brands across moving, containerized storage, and portable storage delivery. Its customer, marketing, and operations data sat in systems that did not talk to each other, so no one could see the acquisition journey end to end.

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Client: National logistics, transportation and storage operator
Industry: Industrial
Services: AI Transformation AI Agents Harness Engineering Infrastructure Engineering Integrations Workflow Automation Customer Intelligence & Data

The challenge

This national logistics, transportation and storage operator runs multiple brands across moving, containerized storage, and portable storage delivery. Its customer, marketing, and operations data sat in systems that did not talk to each other, so no one could see the acquisition journey end to end.

Over three separate engagements, the company brought Trellist three separate problems: that data gap, an IT organization under growing demand, and high customer service call volume. The order turned out to matter. Fewer than 15% of enterprise AI pilots ever reach production (DigitalApplied, 2026), and the ones that do usually rest on foundational work done first.

What we did

Trellist took on each problem in turn, and each build became the precondition for the next.

A cross-brand data foundation. We architected a data pipeline and warehouse connecting marketing, sales, and operations sources, with real-time feeds so spend could be adjusted against live performance.
An objective read on engineering. Serving as fractional CTO, Trellist audited the IT division's workflows, capacity, and workload against real demand, then scoped technical and commercial recommendations.
AI built into development and QA. We redesigned the engineering process around AI-assisted workflows, freeing engineering capacity for the company to invest in other projects.
Discovery grounded in call data. Before building for customer service, we used call-tracking data to find the leading reasons customers called, then extended discovery across the full journey, including the web experience.
An AI agent in the service workflow. The agent resolves common issues conversationally, with human-in-the-loop guardrails for anything outside its lane.

The impact, measured

Customer service call volume fell 30%, delivering a six-figure net positive annual ROI and faster resolution for customers.

30%

Less customer service call volume

From an AI agent grounded in real call data

Engagements built in sequence

Data, then engineering, then the AI agent

30% fewer customer service calls and less time on hold for customers.
Six-figure net positive annual ROI from the AI agent.
Engineering capacity freed and reinvested in other projects, with shorter development and QA cycles and faster time to market.
A cross-brand view of the customer journey from first touch to closed sale.
Capability left behind as each engagement closed with enablement so the client's own team runs what was built.

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