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09 · Web & Growth

AI website assistants: Give visitors useful answers from approved sources, with boundaries and a clear route to a person.

Lumaivo designs website assistants that retrieve from approved business content, explain their limits and hand off enquiries with the relevant context. The data and operating controls come before the conversational interface.

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What this service is for

Start with the business meaning, then make the technical route explicit.

A website assistant should reduce friction without inventing answers or collecting unnecessary information. That requires a controlled source set, tested retrieval, clear refusal and escalation behaviour, and an owner for keeping the underlying material current.

We map the questions the assistant may answer, prepare the approved knowledge sources, design the conversation and handoff, and evaluate responses against known examples. Sensitive decisions, binding promises and unsupported technical claims remain outside the automated boundary.

Where it becomes useful

Common use cases, framed around the decision or hand-off.

01

Service guidance

Help a visitor understand relevant services, prerequisites and next steps using approved website and service information.

02

Knowledge support

Answer bounded questions from product, policy or support material with links or citations back to the source.

03

Structured handoff

Collect the minimum useful enquiry context and route it to the right human review queue without impersonating a person.

Method

A controlled route from discovery to an accepted output.

  1. 01

    Bound the role

    Define allowed questions, prohibited actions, audience, escalation triggers and data-collection limits.

  2. 02

    Prepare sources

    Select approved content, split it into useful passages and retain source identity and update ownership.

  3. 03

    Design the flow

    Create concise answer, clarification, refusal and human-handoff patterns in the business voice.

  4. 04

    Evaluate

    Test representative, ambiguous and adversarial questions for groundedness, privacy and safe escalation.

  5. 05

    Operate

    Monitor failed questions and source freshness, then review improvements before changing live behaviour.

Typical deliverables

What the review-ready work can contain.

  • Assistant scope, boundary and escalation specification
  • Approved source inventory and content preparation
  • Conversation and human-handoff design
  • Implemented assistant or review-ready prototype
  • Groundedness, refusal and privacy test set
  • Operating, update and kill-switch runbook

Boundaries

What stays explicit instead of being over-promised.

  • The assistant identifies itself as automated and does not impersonate a named person.
  • Only approved sources support factual answers and service claims.
  • Sensitive, contractual or high-impact decisions route to a human.
  • Conversation data collection and retention are limited and disclosed through the approved privacy route.

Questions before the first conversation

Plain answers, with the limits left in.

Is this just a generic chatbot?

No. The value comes from the approved knowledge sources, business-specific boundaries, evaluation set and handoff workflow. The conversational model is only one component.

Can it answer from private company data?

Potentially, but that requires a separate access, privacy and authentication design. A public website assistant should not expose internal material simply because it exists.

Can it book appointments or create leads?

It can be designed to collect minimal context or call an approved workflow, but consequential actions and outbound replies should follow explicit identity, consent and review rules.

How do you reduce invented answers?

We restrict the source set, require evidence-grounded response patterns, test known failure cases, use refusal and escalation rules, and monitor unanswered questions.

Who updates the assistant?

The operating plan names the owner of each source and the review process for content or behaviour changes. Stale knowledge is an operational risk, not only a model problem.

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Start with the messy version. Make the first useful outcome explicit.

Send the systems, reports, constraints and decisions involved. Lumaivo will help define a bounded first step and the evidence needed to accept it.

Map the first useful step