Glossary
Knowledge gap detection.
Automatic identification of questions your team is asking that the knowledge base can't answer, and topics where the available information is thin, contradictory, or out of date. Tells you what's missing before someone gets burned by it.
Most knowledge bases tell you what's IN them. The dangerous thing is what's MISSING from them. That's the part you never find out about until someone needs it.
What this looks like in practice
Why gaps stay invisible.
A traditional knowledge base reports what it contains. Nobody runs a report on what it doesn't. The first signal of a gap is usually a customer getting a wrong answer, a new hire spending three days finding something that should have taken three minutes, or a deal lost on a question nobody had documented. By then it's already cost something.
What detecting gaps actually requires.
The system has to track what's being asked vs. what it can answer. Patterns in the gap signal where to invest. Five different reps asking the same question this month means the answer should exist and doesn't. Detecting that requires the system to know it failed, not just to return whatever it could.
The next step beyond detection.
Detection is the first move. The bigger one is closing the gap: naming the right expert to fill it, drafting what should be there, prompting them to confirm or edit. Detection without remediation just generates a longer to-do list. The brain that knows what it doesn't know should also know who can fix it.
Knowing what you don't know is a feature.
Simply Asking detects the gaps automatically: questions asked that we couldn't answer, topics where the source material is thin or contradictory. Then it points to the right expert, drafts what should be there, and prompts them to confirm. Every gap fills itself faster than it forms.
Bring your knowledge.Simply ask.
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