Knowledge grounding
Answers drawn from your documentation, product data and policies, with a source link on every response so a visitor can verify it.
AI & Automation
Chatbot development
A chatbot is judged almost entirely on two things: whether it answers correctly from your actual content, and how gracefully it gives up. Most bad chatbot experiences are not wrong answers. They are the loop where the bot cannot help and will not let you reach a person.
So the design starts from the handover. Everything else is grounding the answers well enough that handover is needed less often.
How I work on it
Process
Adjusted to the job, but this is the shape it usually takes.
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Gather the real questions
Past support tickets and chat logs, if they exist. They define scope far better than an imagined list.
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Ground and evaluate
Build the knowledge base, then score answers against those real questions before anything goes live.
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Launch narrow
Start on a limited set of topics the bot handles well, rather than launching broadly and eroding trust in week one.
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Review transcripts weekly
Failures feed back into content and scope. A chatbot that is never reviewed gets worse as the product changes.
Questions
Things people ask
Not if it is built correctly. Those topics are scoped out or answered only from an approved source document, and the system is instructed and tested to say it will connect you with someone rather than guess. This gets verified in evaluation, not assumed.
Less than people expect, but it must be accurate. Twenty well-written FAQ answers plus real product documentation outperforms a large archive of outdated pages. Poor source content is the most common reason a chatbot underperforms.
No, and I would be cautious of anyone who says otherwise. It can absorb repetitive questions and gather context before a handover, which gives your team more time for the cases that need judgement. That is a real saving; full replacement is not a realistic promise.