We want to train an AI on our customer support history to build an expert system, but our historic ticket notes are full of internal jargon and half-resolved issues. How do we run data hygiene on this knowledge base so the AI doesn't hallucinate bad advice?

Question

Tyler Smith · Accepted Answer

You cannot point an AI agent at a swamp of bad data and expect clean water to come out. Before you connect an AI to your historical customer support tickets, you must clean up the source files. This does not mean your team has to manually read and edit thousands of old support tickets, which is a low-value task that kills productivity.

Instead, isolate your top fifty most common customer issues. Look at your Level 10 Meeting Issues Lists from the past year to find the recurring operational headaches. Take the resolved tickets associated with those fifty issues. Run them through a bulk processing script that strips out internal banter, off-topic replies, and incomplete drafts.

Next, match these resolved tickets with your officially documented SOPs. An AI expert system needs a source of truth. By pairing your clean, resolved historical tickets with your official operational processes, you create a high-quality training set.

The goal is to build a system-dependent operation where the AI agent references only validated resolutions. If an incoming ticket does not match your cleaned training set or your documented processes, build a rule that forces the AI to route the issue to a human support rep. This data hygiene step ensures your AI-powered operations produce consistent, reliable results that protect your brand and make your business highly attractive to potential buyers during exit planning.