From Fragmented Notes to Actionable Answers, at Scale

Delayed delivery investigations were slow, inconsistent, and difficult to scale. OnTrac built an AI-assisted triage workflow that handles initial investigation automatically.

The Challenge

A leading automotive manufacturer used a Microsoft Forms workflow to field delayed delivery inquiries, and each one required someone to dig through internal shipment systems, check logistics status across partners, interpret milestone notes, and write a response back to the requester. The manual process wasn't just slow; it produced inconsistent results. Some responses were thorough. Others returned fragmented notes that raised more questions than they answered. As request volume increased, the team had no path to scale without adding staff to a fundamentally repetitive process. 

What OnTrac Built

OnTrac developed an AI-assisted triage workflow that performs initial investigation steps automatically, so human specialists spend their time on complex cases, not routine ones. 

• Ingests incoming requests directly from the Microsoft Forms workflow as they arrive 

• Retrieves relevant shipment records and milestone data from internal systems in real time 

• Applies predefined logic to identify likely delay causes based on available data 

• Generates clear, fact-based response summaries using a large language model 

• Escalates complex or ambiguous cases to human specialists, with pre-assembled context already in hand 

How does AI-assisted triage change the economics of operational investigation workflows?

Investigation workflows, where someone has to gather data from multiple sources, interpret it, and write a clear explanation, are expensive to run manually and nearly impossible to scale. AI-assisted triage handles routine cases automatically, reducing response time and freeing human specialists for work that actually requires judgment. The result is higher throughput without additional headcount and more consistent quality across every case that gets a response.