How to Measure Knowledge Base Coverage
Knowledge base coverage is how much of your real ticket volume your AI can actually answer from grounded content. Measure it by tracking retrieval strength on live tickets, not by counting how many articles you have. Coverage is a function of whether the AI finds a strong source for each incoming question, so a small, tight knowledge base can outperform a large, sprawling one.
Count answers found, not articles written
Article count is a vanity measure. What matters is the share of real questions where retrieval returns a strong, on-topic source. A knowledge base with a thousand articles that misses the top ten ticket types has worse coverage than a lean one aligned to actual demand. Measure against your ticket stream, not your content library.
Turn gaps into a work queue
Every low-retrieval query is a coverage gap and should be logged as one automatically. Cluster those gaps and you get a ranked list of the articles worth writing next, ordered by real demand. This flips knowledge base maintenance from guesswork into a data-driven queue, where you always know the highest-leverage thing to document.
How Resolve surfaces coverage
Resolve treats weak retrieval as a first-class signal: when the knowledge base lacks a strong match, Reva refuses to guess, escalates, and logs the gap. Analytics surface the top recurring issues so you can see which topics drive volume and which lack coverage. Approved resolutions become new articles, so coverage grows toward wherever the real demand is.