AI-Assisted Web Experiences: Smart Search, RAG and Personalisation

Using AI on websites means more than adding a chatbot. AI can support search, content discovery, product recommendations, customer service, form routing and contextual presentation of information.

However, an AI-assisted web experience without accurate data, user control, privacy and measurement may simply create a more complicated interface.

Design AI as a function that helps users complete a specific task, rather than a flashy feature.

What is an AI-assisted web experience?

The system generates different recommendations or content based on the user’s behaviour, query or context.

Examples:

• semantic site search,
• personalized product suggestion,
• AI-powered customer assistants,
• Product filtering in natural language,
• intelligent form routing,
• dynamic help content,
• RAG-based knowledge assistants.

Identify the User Problem First

“Let’s add AI to our website” is not a good product requirement.

First, the problem must be clearly defined:

• Are users unable to find what they need?
• Is the product catalog too large?
• Are the same questions coming to customer service?
• Is form abandonment high?
• Does the user not understand which service is appropriate for him?

AI is worthwhile only if it delivers a measurable benefit for one of these problems.

Semantic Search

Traditional site search may require an exact keyword. Semantic search interprets the query’s meaning and context to return more relevant results.

A user might enter “a box to organise towels in a small bathroom” instead of a product code.

RAG Based Corporate Assistant

Retrieval-Augmented Generation (RAG) can ground a model’s answers in selected company information sources.

Sources can include:

• product documentation,
• Help Center,
• FAQs,
• corporate procedures,
• Technical Documents

These are possible sources.

This can create a more controlled business experience than letting a general-purpose model answer freely.

AI Assistant Shouldn't Answer Every Question

It should be defined what questions the model is authorized to answer.

For example:

• product information -> AI can answer,
• order status → check through a secure API,
• account changes → authentication required,
• Legal or critical decision-making may need to be directed to a human representative.

Human-in-the-loop

AI does not have to be the final decision maker of every process.

In high-risk or uncertain situations:

1. AI can classify the request,
2. prepare a draft response,
3. a human representative can review it,
4. The final response can be sent to the user.

Personalisation and profiling are not the same

Collecting too much data to personalize the user experience is not the right approach.

When designing an AI usage scenario, it should also be evaluated which data is really necessary and for what purpose this data is processed.

Zero-party and first-party data

Personalisation does not have to rely on covert user tracking.

Using the user's clearly stated preferences or appropriate data that the company obtains within its own service relationship may provide a more controlled approach in some scenarios.

AI Hallucination Risk

Generative AI can produce false or unsourced statements.

Do not treat model output as an authoritative record for prices, contracts, warranties, regulations, specifications or transaction status.

Citing sources

Showing which corporate document the answer is based on in information assistants can increase user confidence and facilitate the verification process.

The Role of API Integration

The AI assistant is limited if it produces text alone. Safe integration is required to connect to the actual business process.

For example:

• CRM,
• stock system,
• order infrastructure,
• support ticket systems,
• Product Catalog

These systems can be integrated through controlled API access.

Do not use agents without permission controls

If an AI agent acts for a user, explicitly limit which actions it may perform and under what conditions.

Additional verification may be required in high-impact transactions such as payment, account deletion, data change, or order cancellation.

Show AI Uncertainty in UX Design

Displaying AI answers and verified system information identically can create false confidence.

Where necessary, clearly show users:

• that the answer is AI-generated,
• sources,
• the option to switch to human support.

These details can be presented explicitly.

AI success requires performance measurement

“The AI feature is being used” is not a success metric.

Depending on the use case, monitor:

• search success rate,
• self-service resolution rate,
• reduction in support tickets,
• conversion rate,
• average task completion time,
• rate of escalation to a human,
• incorrect-answer rate.

These metrics can be tracked.

A/B testing and controlled rollout

AI features do not need to be opened to all users at the same time.

Pilot work in small user group can measure quality, cost, latency and error rates.

Checklist for AI Web Experience

• Has a clear user problem been defined?
• Is AI really necessary?
• Is source data reliable?
• Have permission boundaries been defined?
• Is the risk of hallucination managed?
• Is escalation to a human available?
• Has personal data usage been restricted?
• Have success KPIs been defined?
• Is API access secure?
• Is a fallback available if an error occurs?

The B10 Approach

B 10 Digital Agency considers AI web experiences beyond chatbot design. It addresses user journeys, data, RAG, API integration, security and measurement together.

• Artificial Intelligence & Automation Solutions
• Design & User Experience (UX/UI)
• API & System Integrations
• Web Development & Secure Infrastructure

Conclusion

AI web experiences create value by helping users complete tasks more accurately and quickly. Without reliable data, limited permissions, human oversight and measurable KPIs, added AI features can create new UX and security debt.