What a bot must not say in a clinic differs from what it must not say at a car dealership, and a bank is different again. This section takes each industry on its own terms: what customers ask, what the bot collects, at which step — a viewing, an appointment, a test drive, a booking — it hands over to a person, and where it has to stop in regulated work.
Each article is written for someone working in that industry, not a general chatbot text with a sector's name added. To discuss a scenario for your sector, get in touch; the channels' general capabilities are on the features page.
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A dental clinic chatbot separates urgent pain from planned treatment: intents, risky answers, the emergency rule, prices and the appointment flow.
A recruitment chatbot answers vacancy questions, collects applications and schedules interviews — while the selection decision always stays with the recruiter.
How a hotel booking chatbot turns enquiries into bookings: four questions, the bot's limit on availability and price, groups, night and languages.
An e-commerce chatbot covers the whole store lifecycle: choice before purchase, the checkout boundary, status and returns, and one customer history.
A SaaS chatbot catches trial users the moment they get stuck: trial intents, activation blockers, the support–demo line and the success handoff.
An aesthetic clinic Instagram chatbot takes ad DMs to a consultation: comment to DM, procedure choice, photo policy, consent, price and stopping points.
A professional services chatbot for law, accounting and consulting: intake, qualification, a brief for the expert, a document list and the advice boundary.
A logistics chatbot separates two streams: it answers shipment status from live data and passes new quote requests, complete, straight to sales.
A quote conversation that collects eligibility inputs and hands off correctly — with the boundaries around pricing, coverage statements and claims.
Separating safe informational automation from regulated and account-specific actions — the three tiers, the identity problem and the fraud path.
Intent routing and structured capture for tenant issues — separating emergencies from routine requests, and what a maintenance ticket must contain to be actionable.
Separating simple reservations and FAQs from the operational data that has to be live and correct — allergens, availability and the moments a person must answer.
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