Support staff spend half their time searching: the manual for the old model, the service bulletin, the ticket of a colleague who had already seen the same fault. Here the system does the searching, and the answer says where it comes from.
What you need to fix a fault usually exists already, but it is scattered: manufacturer PDFs, shared folders, a wiki nobody finished, thousands of closed tickets. And above all the heads of the two most experienced technicians, the ones everybody calls.
So the same questions get solved from scratch every time, new hires take months to become independent, and when the expert technician is on holiday support slows down.
Manuals, technical sheets, bulletins, closed tickets, scanned ones too. The index updates when the source documents change, with nothing to re-upload by hand.
Error codes, serial numbers and model names are searched by exact match, the rest by meaning. It is the part a purely semantic search gets wrong.
Document, page, ticket. The technician opens the original and checks before acting, and if there is no source the system says so.
In the ticketing system, in an internal chat, on a tablet on site. One more search tool to open is a tool nobody uses.
These are the numbers we set in discovery, before writing any code. The starting value is measured on your data, and the prototype is judged against it.
From opening to closing the ticket, by category. Recurring questions are the first to come down.
How many requests first level closes on its own. That is where expert technicians get their time back.
A sample checked by hand every month. It tells you when to trust it, and uncovers the documents that are missing.
If the documentation does not exist or is wrong, the system gives wrong answers with confidence. In that case you start by putting the documents in order, and sometimes that is enough.
How the month went, which orders are stuck, what is missing from stock. Questions in plain language to the assistant you already use, with the answers taken from the ERP.
Everyone is pitching something with AI and nobody in the company can evaluate it. A technical counterpart who lines up the use cases and decides with you what to buy and what to build.
A free 30-minute call, with no commercial follow-up unless you want it.