AI Shopping in E-Commerce: Chatting with Your Own Data
With our AI Shopping solutions for e-commerce, product knowledge and manufacturer information become an interactive experience: powered by Retrieval-Augmented Generation (RAG), dialogue-based shopping assistants answer customer queries in real time — precisely, context-sensitively, and based on your own data and content.
Whether product catalogue, database, product detail page, PIM or MDM: AI Shopping transforms your existing data and content pools into intelligent, dialogue-based information systems. The shopping assistants are ready to use out of the box, easily integrated via API, and can be tailored to suit your brand identity and target audiences.
Why AI Shopping Assistants? Because a compelling customer experience in e-commerce makes all the difference!
Chatbots like ChatGPT are impressive – but they tend to produce distorted information and hallucinations. For e-commerce providers who prioritise precision and reliability, this poses a real challenge. That’s where our AI Shopping Assistants come in: they combine the power of generative AI with the reliability of your own product data and information.
Before generating a response, the system analyses your internal data sources – from product catalogues and PIM or MDM systems to support content. The result: context-sensitive, precise answers in natural language, drawn directly from your existing content.
What sets our AI Shopping solutions apart: they combine semantic search, neural retrieval, and intelligent parsing to enable a deep understanding of even the most complex product structures and relationships. This means your content isn’t just found – it’s delivered intelligently and interactively, through assistants that truly support your customers.
AI Shopping in Practice: How Shopping Assistants Support E-Commerce
A customer is searching for an ergonomic office chair with an adjustable backrest and breathable material. Up to now, they would have had to click through various product searches and PDFs – often with unclear or incomplete results.
With an AI Shopping Assistant, this process is fundamentally improved: the customer simply asks their question directly in the system – and receives a precise, easy-to-understand, and reliable answer within seconds. The underlying generative AI draws on internal product data, manufacturer information, catalogues and databases, filters the relevant content, and generates a tailored natural-language response – well-structured and summarised.
The key advantage: the content is based exclusively on verified, up-to-date data – meaning AI Shopping delivers not only fast but also factually accurate product information, tailored to the customer’s specific needs.
And it goes further: AI Shopping Assistants link directly to the relevant product, point to detailed information, and enable immediate action – such as making a purchase or getting in touch.
This creates a whole new kind of customer experience in e-commerce – interactive, personalised, and conversion-boosting. The AI-based shopping assistant becomes a digital sales companion, enhancing advice, service, and conversion rates.
A Wide Range of Use Cases for AI Shopping in E-Commerce
AI Shopping Assistants are opening up new avenues for greater interaction in e-commerce:
AI Shopping Assistants – Making Product Information Interactive In E-Commerce
With our intelligent AI Shopping assistants, you bring structure to your product data and make revenue-relevant e-commerce information accessible. Fast, flexible, and tailored to your needs:
Why choose AI Shopping Assistants from Retresco?
|
Retresco AI Shopping Assistants
|
ChatGPT & comparable systems
|
||
|---|---|---|---|
| Architecture | Dynamic & modular: Adapts to the query context in real time – thanks to integrated retrieval and generation components | Linear & static: Responses follow a fixed sequence – without accounting for e-commerce-specific contexts | |
| Retrieval processes | Context-based selection of relevant product data, formats, and response types – ideal for sophisticated shopping assistance in e-commerce | Limited query logic and adaptability to different questions and individual customer needs | |
| Data integration | Standard API for PIM, MDM, CMS, shop systems, and internal content pools – specially developed for content workflows | Limited integration capabilities with internal e-commerce systems or product databases | |
| User interaction | Interactive dialogue management & feedback loops – continuous optimisation of the AI Shopping experience based on real customer queries | One-dimensional question-and-answer structure – without company-specific feedback mechanisms or personalisation | |
| Context understanding | Semantic search, domain-specific knowledge, and multi-step reasoning for precise answers – even with complex product queries | Limited context understanding due to reliance on general training data | |
| Content quality | Flawless answers based on verified content and original sources – ideal for products requiring detailed explanation in e-commerce | Risk of biased or incorrect content due to external and unverified data sources | |
| Personalisation | Fully customisable for e-commerce providers: tailored to product data, categories, and target audiences | Limited configurability, hardly suitable for shopping assistance or e-commerce-specific use cases | |
| Automation | Automated content delivery, prioritisation, and output – aligned with strategic goals and information value | Pure text output without content weighting or strategic relevance | |
| Scalability | Optimised for large data volumes, diverse content formats, and high customer volumes in e-commerce | Limited performance with data-intensive queries or high volumes of simultaneous customer interactions | |
| Analyse & Insights | Precise KPIs and usage data for optimizing the AI Shopping application and content delivery | No or only limited analysis of customer behaviour and response quality | |
| Intuitive user interface | Fully customisable to corporate design and brand worlds – for consistent customer experiences in e-commerce | Uniform interface without extensive company-specific customisation options | |
| LLM integration | Flexible: Integration of any large language model (proprietary or open source) possible | Limited to predefined language models provided by the LLM operator | |
| Languages / localisation | Multilingual output with country-specific SEO and linguistic fine-tuning | Mostly English-centric, translations lacking cultural or product-specific nuances | |
| Updates | Regular updates focused on e-commerce requirements and customer feedback | Standardised updates, mostly without industry focus or company-specific roadmaps | |
| Support | Personalised support from German-speaking AI experts with deep industry knowledge | General online support without industry-specific focus or individual guidance | |
