A national logistics, transportation and storage operator came to Trellist with three separate problems over three separate engagements. Only in hindsight do they read as one project: each build turned out to be the precondition for the next.
That sequence is worth examining, because it is the part most AI programs skip. Fewer than 15% of enterprise AI pilots ever reach production (DigitalApplied, 2026). The ones that do tend to share a quiet advantage — somebody did the unglamorous work first.
The company ran multiple brands across moving, containerized storage and portable storage delivery. Customer, marketing and operations data lived in systems that did not talk to each other, which meant no one could see the acquisition journey end to end.
Trellist architected a full data pipeline and warehouse connecting marketing, sales and operations sources, with real-time feeds so spend could be adjusted against live performance rather than last month’s report. The result was a cross-brand view of the customer journey from first touch to closed sale.
This engagement predates the name we now give it. It is what our AI-Powered Transformation methodology formalizes as Define and Audit: establish what data exists, how it moves, and where the valuable connections are, before anything gets automated on top of it.
As demand on the IT organization grew, leadership needed an objective read on whether developer capacity actually matched operational and project demand. Serving in a fractional CTO capacity, Trellist audited the division end to end — workflows, capacity, and workload against real demand — then scoped a set of technical and commercial recommendations.
The build embedded AI directly into development and QA, shifting routine engineering work into AI-assisted workflows. The distinction that mattered: the process was redesigned around what AI makes possible rather than having AI bolted onto the existing one.
This is the shape of a Fractional AI Team engagement and our workflow modernization practice: pick the highest-value workflow, rebuild it, and transfer the capability so the client’s own team runs it afterward.
Customer service call volume was running high, straining the support team and raising operating costs. The instinct in 2026 is to reach for a chatbot immediately. Trellist started with the call-tracking data instead, identifying the leading reasons customers were picking up the phone, then extending discovery across the full customer journey including the web experience.
With the actual drivers identified, the scoped build was an AI agent that resolves common issues conversationally, with human-in-the-loop guardrails for anything outside its lane, deployed straight into the customer service workflow.
Grounding the agent in real call data — rather than assumptions about pain points — is why it resolved the issues actually generating volume.
The agent worked because the engineering organization could support it. The engineering redesign worked because the data foundation made capacity and demand legible in the first place. Run in reverse, the same three projects would likely have produced a pilot that impressed in a demo and quietly expired.
Every engagement above ran the same five steps — Define, Audit, Propose, Execute, Enablement — with the last one carrying the most weight. The goal is to build the hardest technical foundation, hand over the controls, and leave capability behind rather than dependency.
Related capabilities: Harness Engineering, AI Agents, Fractional AI Team, and Workflow Modernization.