AI Shopping in E-commerce: Chatting with your own data
Our AI shopping solutions for e-commerce bring product knowledge and manufacturer information to life through interactive experiences. Based on Retrieval Augmented Generation (RAG), interactive shopping advisors answer customer questions in real time – precisely, contextually, and using your own or external data sources and content. Boost engagement and drive repeat purchases.
Whether it’s a product catalog, database, product detail page, or PIM system, AI Shopping transforms existing data and content into intelligent, dialogue-driven shopping experiences. The AI advisors are ready to use immediately, can be integrated via API, and enriched with content.
Benefit from comprehensive quantitative and qualitative analyses directly from the interactive AI shopping advisor: performance KPIs, transparent usage patterns, and valuable insights from real user questions and product interests.
Why AI Shopping? Because a compelling customer experience makes all the difference in e-commerce!
Generalist chatbots like ChatGPT are impressive – but they tend to provide distorted information and hallucinations. This poses a real challenge for e-commerce providers who prioritize precision and reliability. Our AI Shopping advisors help: They combine the power of generative AI with the reliability of your own product data or external manufacturer information.
Before generating an answer, the system analyses your data sources – from product catalogues and PIM and MDM systems to support content and verified manufacturer data. The result: context-sensitive, precise answers in natural language, directly from your existing content.
What makes our AI Shopping solutions special is that they combine semantic search, neural retrieval, and intelligent parsing to enable a deep understanding of even complex product structures and relationships. This ensures your content is not only found but also presented intelligently and interactively – through shopping advisors that provide effective support.
AI Shopping in Practice: How Shopping Advisors Support E-Commerce
A customer is looking for an ergonomic office chair with an adjustable backrest and breathable material. Until now, they would have had to click through various product searches and PDFs – often with unclear or incomplete results.
With an AI Shopping advisor, this changes fundamentally: The customer asks their question directly in the system – and receives a precise, understandable, and reliable answer within seconds. The underlying generative AI accesses internal product data, manufacturer information, catalogues, and databases, filters out relevant content, and generates a personalized answer in natural language – presented and summarized.
The advantage: The content is based exclusively on verified, up-to-date data – AI Shopping thus delivers not only fast but also factually accurate product information, tailored to the customer’s specific needs.
And what’s more: The AI Shopping advisors link directly to the right product, provide access to detailed information, and enable direct action – such as making a purchase or contacting the company.
Diverse use cases for AI shopping in e-commerce
AI Shopping advisors open up new avenues for more interaction in e-commerce:
AI Shopping Shopping Advisor – Making product information interactively usable in e-commerce
Our intelligent AI Shopping advisors bring structure to your product data and make sales-relevant e-commerce information available. Fast, flexible, and tailored to your needs:
Why use Retresco's AI Shopping advisor?
|
Retresco AI Shopping Shopping Advisor
|
ChatGPT & similar systems
|
||
|---|---|---|---|
| Generation components for targeted, contextual purchasing advice in e-commerce | Training data generated – without dedicated customisation for commerce use cases. | ||
| Retrieval processes & response depth | Agent-driven queries of internal and external product and content APIs deliver structured, context-specific answers and recommendations. | Retrieval and tool usage are possible, but mostly generically configured and not optimised for e-commerce consulting or product logic. | |
| Data integration | Standardised API integration for PIM, MDM, CMS, shop systems, DAM & content pools – specifically for product and commerce data models | Integrations are technically possible, but must be implemented on a project-specific basis and are not always designed for e-commerce data structures. | |
| User interactions | Conversational shopping with structured dialogues, follow-up questions, decision trees & feedback loops for better purchasing decisions | Generic chat interaction without integrated commerce dialogue logic or native feedback processes for e-commerce. | |
| Contextual understanding | Domain-specific semantics, product logics & multi-level derivations for pinpoint-accurate consulting | General language comprehension; commerce context must be provided prompt- or tool-based in each case. | |
| Content quality & safety | Answers from all desired product and content sources (Grounded Generation) | Depending on the setup, a mix of training knowledge and unverified sources is used; company data must be connected separately. | |
| Personalisation | Individually configurable to product range, categories, target groups, use cases & brand worlds | Personalisation is possible, but not natively designed for shopping advice or product range structure | |
| Frontend widgets | Out-of-the-box widget for website & shop: Branding, colours, logos, fonts, text labels & disclaimer available | No native shop widget solution; the frontend must be individually developed and designed | |
| Chat history | Structured, nameable chat histories for businesses – resumption possible at any time | Chats are available, but mostly unstructured for commerce journeys or business analysis | |
| User feedback | User ratings & comments via a dedicated module – basis for continuous optimisation & training | Feedback can only be captured indirectly or externally; there is no integrated commerce feedback loop | |
| Insights & Analytics (Conversational Analytics) | Detailed KPIs on questions, products, purchase interests & usage – available in the frontend or automatically via API | Usage analytics depend on your own tracking; no specific shopping or product insights are provided. | |
| Automation | Strategic content prioritisation and response logic based on conversion goals, product range structure, and business rules | Generative text output without native commerce prioritisation or business logic | |
| Scalability | Optimised for large product catalogues, variant logic, attributes & high shop traffic loads | Scalability is possible, but it is not specifically optimised for product data complexity or commerce traffic | |
| Interface | Seamlessly integrated into shop UX and corporate design – consistent brand experiences in conversational commerce | Uniform standard UI or individually developed interface | |
| LLM connection | LLM-agnostic: Integration of any proprietary or open-source models depending on data protection, cost, or quality requirements | Commitment to the model ecosystem and infrastructure of the respective provider | |
| Localisation | Multilingual consulting with SEO and country-specific terminology as well as product-specific tone | Multilingual support is available, but without specific commerce localisation or SEO fine-tuning. | |
| Updates | Roadmap focuses on e-commerce and use cases in conversational commerce | Generic and cross-industry model updates | |
| Support | Personal support from AI and e-commerce experts, including use case and content consulting | Platform support without specific commerce or product range consulting | |
