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Use cases / Use case · Customer service

AI assistants for customer enquiries

Most Singapore SMEs answer the same twenty questions all week — slowly during the day, not at all after hours. Speed is revenue here: enquiries answered in minutes convert at a different rate than enquiries answered tomorrow. This is the use case with the clearest money attached, and the clearest way to do it badly.

01Sound familiar?

  • Enquiries arriving at 10pm and waiting until opening
  • Staff hours consumed by opening-hours and price questions
  • Inconsistent answers depending on who's on shift
  • A previous chatbot that made customers angrier

02What an assistant does here

  1. 01 Answers from your verified facts

    Products, prices, policies, delivery areas — loaded and maintained by you, cited in answers. Grounding is the entire difference between this and the chatbot people hated.

  2. 02 Says 'I don't know' honestly

    Questions outside its facts get an honest handover, not improvisation. An assistant that guesses your refund policy is a liability generator.

  3. 03 Hands over with context

    When a human is needed — complaints, exceptions, judgment — the conversation arrives with them, so the customer never repeats themselves.

  4. 04 Reports what it couldn't answer

    The weekly unanswerable-questions list is free product research: missing pages, unclear pricing, demand you didn't know existed.

03The honest fine print

  • Setup is writing your facts down — most businesses discover their pricing page and their practice disagree. That reconciliation is unavoidable and valuable.
  • Refunds, complaints, and anything medical/legal/financial must route to humans by rule, not by the assistant's judgment.
  • Conversations contain personal data: PDPA applies, retention needs a policy, and customers shouldn't have to guess they're talking to software.

04Concepts designed for this job

05Go deeper

Eight questions, applied to your situation: run the Assistant Finder for a structured brief — no email required.