Synthetic non-agentic case: External customer-information chatbot

Fictional example. The retailer, users, conversation set, results, and decision are synthetic. They illustrate control design and do not prove that a chatbot improves service.

Dominant pattern: conversation. Secondary pattern: retrieval. Level: A1. The chatbot answers from approved public information and transfers to a human. It cannot authenticate a customer, access an order, issue a refund, send a message, or change any system.

1. Starting point

Alpina Outdoor GmbH is a fictional Swiss online retailer serving customers in Switzerland and the European Union. Its team receives about 1,100 questions per month. Roughly 620 concern store hours, delivery zones, returns, size guides, and public product manuals. Account, payment, complaint, and warranty cases need a person.

The target is: answer bounded public-information questions or route the person to a reachable human channel. Deflection is not the primary success metric.

2. Interaction and legal boundary

The interface explains that the person is interacting with AI, what data should not be entered, how messages are used, and how to reach a human. The disclosure must remain visible on mobile and be understandable with a screen reader.

The Article 50 EU role analysis, Swiss transparency analysis, privacy notice, retention, provider reuse, processor, transfer, and accessibility decisions are dated separately. One jurisdiction's notice does not replace the other.

Prohibited topics include personalized medical or safety advice, account status, payment, refunds, warranty decisions, complaints, legal interpretation, and any promise about price or delivery. These topics trigger a human handoff.

3. Frozen conversation evaluation

The gate set contains 160 scripted conversations, including 40 multi-turn cases, 24 handoff cases, 20 adversarial or abusive cases, 16 unsupported questions, and language and accessibility slices.

Synthetic frozen-set result

The first interface produces 88% disclosure recognition on a mobile comprehension check and fails the gate. After the disclosure is rewritten and placed before the first input, a new frozen usability sample reaches 39/40, or 97.5%.

On the full conversation set, supported answers reach 132/136, or 97.1%; all 24 mandatory handoffs are offered; 23 of 24 handoff links complete successfully; and no invented price, deadline, or warranty appears. The failed handoff is treated as a service incident and blocks live use until corrected.

4. Pilot design

After the handoff defect is fixed and retested, the first stage runs in shadow: staff see the customer question, answer normally, and later compare the chatbot proposal. A limited A1 pilot may then serve only approved public-information intents during staffed hours.

The ledger preserves every request, locale, eligibility decision, disclosure version, cited page, answer, refusal, handoff offer, handoff completion, human correction, complaint, and deletion event. It never reports only the questions the chatbot chose to answer.

5. Decision

Decision: correct and rerun the handoff gate before any external pilot.

The answer and disclosure metrics do not override a broken recourse channel. External use remains prohibited until the exact handoff path passes on desktop, mobile, keyboard-only navigation, and the supported screen-reader combination.

6. Transfer limits and source anchors

The example does not claim a deflection rate, cost saving, or customer outcome. Its frozen conversations and results are synthetic.

The legal prompts are anchored in the Swiss FDPIC guidance on AI and data protection and the European Commission's final Article 50 transparency guidelines. Evaluation follows the conversation profile in the AI use-pattern guide.

7. Evidence pack

Retain role and jurisdiction analyses, notice versions, comprehension tests, approved pages, frozen conversations, accessibility results, handoff monitoring, provider terms, retention proof, complaint route, incidents, and the gate record.