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Choosing customer support AI tools — the resolution rate trap
Product pages in this category quote automated resolution rates freely. Those numbers rarely reproduce in your environment, so knowing how to read them matters.
Reading the rate Korean responses Real product list
The short answer
Resolution rate depends heavily on the mix of enquiries. The same tool produces very different numbers for a service dominated by simple lookups versus one with complex cases. The vendor’s figure is a reference; yours has to be tested.
So the verification is fixed: put last month’s 100 real enquiries through the tool and have a person score how many were resolved correctly without human involvement.
Candidates by use
| Use | What to look at | Real products |
|---|---|---|
| Ticket handling and agent assist | Integration with your helpdesk | Zendesk |
| Autonomous resolution | Resolution rate and escalation quality | Intercom Fin |
| Building enterprise agents | Design freedom and on-premise options | Kore.ai · Cognigy |
| Multi-channel messaging | Coverage of Korean channels | Gupshup |
| Combined chat and voice | Voice quality and call handoff | Yellow.ai |
What to check
How to read a resolution rate
Ask three things of any vendor figure: what enquiry mix produced it, what "resolved" means, and whether customer satisfaction was measured alongside.
The second matters most. Some definitions count a customer giving up and closing the window as resolved. Not escalated and resolved are different things.
Producing your own number is simple: run 100 of last month’s enquiries through and have a person classify each as (1) resolved correctly, (2) partially resolved, (3) answered wrongly, or (4) escalated. The share in category 3 decides whether you can adopt.
Frequently asked questions
Can we reduce agent headcount?
Usually not at first. As simple enquiries automate, agents concentrate on complex ones and handling time per case rises. A realistic first goal is faster response and out-of-hours coverage rather than headcount.
What if it answers wrongly?
Measure the wrong-answer rate before adopting and set a ceiling. Then design it to hand over when unsure — escalating beats being confidently wrong.
Our knowledge base is a mess.
Organising the documents that answer your top 30–50 enquiries is enough to start. Making a full clean-up a precondition prevents starting at all.
How do we compare Korean quality?
Put 20 real enquiries through each candidate and read the answers side by side. Separate from accuracy, judge "does our brand talk like this" — the differences become obvious.
Product list
Narrow the candidates
The AI product directory filters customer support products by pricing type and Korean support.