An IT company’s support team was spending 25 minutes per ticket on context they should have had instantly.

Every time a new ticket came in, technicians spent 15–25 minutes searching through scattered notes, old tickets, and email threads to understand the client’s history. Context was spread across three different systems with no way to connect them. The result: slower resolution times, frustrated technicians, and clients repeating themselves on every call.

Tickets arrived as plain text with no history attached. Technicians manually searched a ticketing system, a separate CRM, and an email inbox to piece together what happened last time. Most gave up after a few minutes and just asked the client to explain everything again.

An AI middleware layer that triggers on every new ticket. It pulls the client’s full history from the ticketing system, CRM, and email archives, then uses GPT-4 to generate a structured context summary: recent issues, recurring patterns, relevant notes, and suggested next steps. The summary appears as an internal note on the ticket before a technician even opens it.

Technicians now open a ticket and immediately see a concise history brief. No more digging. No more asking clients to repeat themselves. Context time dropped from 25 minutes to roughly 2 minutes — and that’s mostly reading time, not searching.

Context gathering went from 25 minutes to ~2 minutes per ticket. Technician satisfaction improved noticeably within the first week. Client feedback shifted from “I already told you this” to “how did you know that?” The system paid for itself within the first month through time savings alone.