AI shopping assistant launch checklist: 20 steps from brief to production

Launching a conversational shopping assistant for fashion retail requires more than selecting an LLM. From product data preparation and disclosure requirements under the EU AI Act to guardrails, integration, testing and measurement, deployment spans legal, technical and commercial domains. This checklist covers 20 steps from brief to production, grounded in implementations by Ralph Lauren, Zalando and the regulatory framework now in force across the EU.

What product data does a conversational assistant need?

An AI shopping assistant needs a current view of product identity, variants, attributes, price, stock, availability, delivery, policies, and relationships between products. Clean, structured product data is no longer optional. Before selecting a model or writing prompts, audit catalogue completeness.

• Product identity: stable ID, title, category, URL, image and parent product relationship for every SKU and variant.
• Attributes: size, material, colour, fit, occasion and compatibility fields that match how shoppers ask questions.
• Pricing and availability: current price, stock level, delivery promise and cut-off time, updated at the frequency the assistant requires.
• Structured data markup: Schema.org Product, Offer and AggregateRating on every product page.
• Content depth: descriptions reflecting shopper intent, not only marketing language, and review sentiment for trust signals.

Merchants with 95 per cent or more data fill rates on core attributes see dramatically higher AI agent visibility, as agents need rich product details and complete product catalogues to surface the right product for each customer query.

How do you select the right LLM for fashion retail?

Model selection balances capability, cost, latency and compliance. Ralph Lauren launched Ask Ralph, a conversational AI shopping experience powered by Microsoft Azure OpenAI, in September 2025. Enquiries to the Zalando Assistant are three times longer than traditional search terms, producing more intensive interaction, larger basket sizes and higher return visit rates; 22 million people used the assistant in 2026.

Evaluate models on four criteria. First, understanding fashion context: can the model interpret subjective requests such as "something for a summer wedding" and map them to product attributes? Second, product catalogue integration: does the model support retrieval augmented generation to ground responses in verified inventory data? Third, cost and latency: smaller models suit high volume customer service, whilst larger models handle creative outfit assembly. Fourth, provider terms: confirm data residency, usage rights and compliance with UK GDPR and EU AI Act obligations.

What does the EU AI Act Article 50 require for chatbots?

The transparency obligations under Article 50 of the AI Act apply from 2 August 2026: disclosure of interaction with an AI system, machine readable marking of generated or manipulated content, disclosure of deep fakes and public interest text, and notification concerning emotion recognition or biometric categorisation systems. For conversational shopping assistants, the provider obligation is straightforward.

The disclosure must happen at the beginning of the interaction, before or as the conversation starts; a disclosure buried in terms of service or privacy policy is not sufficient. The disclosure needs to appear from the very first interaction, in a way that is clear and accessible. Implementation examples include a visible label at chat launch ("You are chatting with an AI assistant") or an audio announcement for voice channels.

The only exemption applies where it is obvious to a reasonably well informed, observant and circumspect person that they are interacting with an AI. The European Commission adopted its guidelines on the transparency obligations of Article 50 on 20 July 2026. National market surveillance authorities can enforce these obligations from 2 August 2026.

How do you build guardrails to prevent hallucinations and policy violations?

The solution to preventing the model from extrapolating beyond known facts lies in grounding LLM responses in verified product data and implementing safeguards. Safe AI usage means building guardrails into the model: defining what it can and cannot discuss, what data it can access, and how it escalates to human agents when needed.

Guardrails operate at three layers. Input validation blocks prompt injection attempts and filters queries outside the assistant's scope (for example, requests for medical or legal advice). Retrieval constraints restrict the model to answering only from the product catalogue, pricing API and approved policy documents. Output validation checks generated responses against product data before display, flagging claims that cannot be verified and escalating to human agents when confidence falls below a threshold.

The three gate guardrails architecture (automated rule checks, human sampling at 10 to 20 per cent, quarterly legal audits) remains the production standard for ecommerce AI content generation.

What are the essential integration and testing steps?

Integration connects the assistant to product catalogue, inventory, order management, customer service and analytics systems. Testing must cover accuracy, latency, edge cases and accessibility before launch.

Pilot with real users before full deployment. Test with real users before full deployment to catch surprises early. Run scripted test conversations per channel and language, and conduct accessibility checks to meet EU requirements.

Which KPIs measure conversational assistant performance?

For conversational AI and service agents, track resolution time, containment rate, customer satisfaction (CSAT) and net promoter score (NPS). Metrics include customer satisfaction, resolution rate, average order value, and how many queries the bot handles without human help; tracking these will show where the bot adds value and where to improve.

Fashion specific indicators include outfit completion rate (percentage of sessions where the assistant recommends multiple coordinated items) and style refinement depth (average number of clarifying questions per session). Zalando reported a 23 per cent increase in product clicks and a 40 per cent uplift in wishlist adds for users engaging with the assistant.

• Customer satisfaction (CSAT or NPS): overall experience quality and likelihood to recommend.
• Conversion rate: percentage of assistant sessions resulting in purchase.
• Average order value (AOV): revenue per transaction for assistant driven sales.
• Containment rate: percentage of queries resolved without human escalation.
• Resolution time: average duration from first message to issue closure.
• First contact resolution: percentage of issues solved in a single session.

Establish baseline performance before launch, set targets aligned with business objectives, and review weekly during the first month to identify configuration gaps and training needs.

What is the complete 20 step launch checklist?

This checklist synthesises product preparation, legal compliance, technical build, testing and measurement into a linear launch sequence.

• 1. Audit product data completeness: measure attribute fill rate, identify gaps in size, material, colour, fit and delivery data.
• 2. Standardise catalogue structure: consistent taxonomy, normalised naming, complete parent child variant relationships.
• 3. Implement Schema.org markup: Product, Offer, AggregateRating on every product page.
• 4. Define assistant scope: which categories, brands, customer service topics and languages to cover at launch.
• 5. Select LLM provider: evaluate on fashion context understanding, RAG support, cost, latency and compliance.
• 6. Design Article 50 disclosure: visible or audible statement at first interaction, accessible format, tested per EU guidelines.
• 7. Build retrieval pipeline: connect assistant to product catalogue, inventory and policy knowledge base with real time or near real time refresh.
• 8. Configure input guardrails: block prompt injection, filter out of scope queries, validate customer intent.
• 9. Set retrieval constraints: restrict answers to verified product data, pricing API and approved content sources.
• 10. Implement output validation: check generated claims against product records, flag unverifiable statements, set confidence thresholds.
• 11. Integrate customer service platform: build escalation to human agent with context transfer and conversation history.
• 12. Connect analytics and monitoring: log sessions, track query intent, measure conversion funnel, set error rate alerts.
• 13. Build accessibility features: screen reader support, keyboard navigation, high contrast mode, test per WCAG 2.1 AA.
• 14. Write 50 test queries: cover common use cases, edge cases, ambiguous requests and out of scope questions.
• 15. Run accuracy testing: verify assistant surfaces correct products, accurate pricing and valid recommendations for test set.
• 16. Conduct latency and load testing: measure response time under expected traffic, identify bottlenecks, optimise slow paths.
• 17. Pilot with internal users: staff and beta testers provide feedback on accuracy, usability and tone before public launch.
• 18. Define KPIs and targets: baseline CSAT, conversion rate, AOV, containment rate and resolution time; set launch targets.
• 19. Launch to limited audience: phased rollout to 5 to 10 per cent of traffic, monitor error rates and user feedback daily.
• 20. Review and iterate: weekly performance review for first month, adjust guardrails, retrain on edge cases, expand rollout.

This sequence assumes product data work begins first, as structured catalogue feeds underpin every subsequent step. Legal disclosure design (step 6) must complete before customer facing testing (steps 14 to 17). Analytics integration (step 12) enables the measurement framework (step 18) that guides post launch optimisation (step 20).