What the tool is and why retail needs it
RAG (retrieval-augmented generation) is a combination of search over your data and a language model. The model doesn't “invent” an answer from general knowledge — it first finds relevant fragments in your database (product cards, return policies, FAQs, past support tickets) and formulates the answer strictly from them. For retail and e-commerce, this is the key difference from a “bare” chatbot: the answer can be backed by a link to a specific source, which means it can be verified.
This is a review of open sources on what you can do with this tool in the industry. This is not a description of a KT.Team project, but an analysis of public examples and research.
What task it solves: customer support and content
A typical online-store pain is the flood of identical questions: “where's my order,” “how do I return it,” “will this filter fit this coffee machine,” “is this size in stock.” By industry benchmarks, RAG assistants resolve 40–50% of routine requests without an operator — specifically for repetitive queries: order status, return policy, product specs, delivery terms, basic troubleshooting (Wonderchat, 2025 RAG in Customer Support Benchmark). An important caveat from the same report: the figure applies specifically to routine requests, and the final percentage depends on the request mix of a specific store, not on the choice of model.
The second task is content preparation. The same index over product cards and guidelines helps a content manager quickly assemble a product description, an FAQ answer, a model comparison — with a link to the source of the specs.
Open examples from the industry
Grainger (B2B distribution, MRO). The company deployed RAG on top of a catalog of 2.5M products with ~400,000 daily updates to speed up product lookup for salespeople and call-center operators. It claims “faster and more accurate product search” and handling thousands of queries in real time. The source is Databricks promotional material, so quantitative metrics on accuracy and satisfaction there are limited (ZenML LLMOps Database / Databricks).
Research on graph-enhanced RAG for e-commerce support. The research paper describes a system that builds knowledge from the product catalog and ticket history (50,000 product entities, 2.3M relationships, 500,000 resolved requests) and answers by combining a structured graph with text search. The result versus ordinary document-only RAG: factual accuracy of 91% vs 74% (+23 pp) and satisfaction of 89% vs 67% as rated by 50 experienced support agents (arXiv 2509.14267). This shows that answers can be made verifiable and coherent precisely by tying them to sources.
Both examples confirm the logic: RAG quality in retail comes down not to the model but to the data — product cards, guidelines, tickets — and to the discipline of keeping them current.
How it works technically
Minimal loop:
1. Sources. Product cards (attributes, compatibility, availability), policies (returns, delivery, warranty), FAQ, a base of resolved tickets.
2. Indexing. Texts are split into chunks, turned into embeddings, and stored in a vector index. The catalog updates incrementally — at Grainger that means hundreds of thousands of changes per day.
3. Search. For a customer's question, the system retrieves relevant fragments (hybrid: keywords + semantics; in advanced variants — a graph of "product–attribute–compatibility" relationships).
4. Generation with a citation. The model formulates an answer strictly from what was found and attaches a link to the source.
5. Observability. Logged: which sources were pulled in, answer quality, latency, the share of requests closed without an operator.
Where the limits and risks are
- Garbage in — garbage out. An outdated product card or a contradictory return policy will produce a confidently wrong answer. RAG doesn't cure messy data — it exposes it.
- A citation is mandatory. Without a source link, the main advantage is lost — verifiability and trust.
- Not all routine is the same. Some queries need access to order/payment status — that's backend integration, not just RAG over documents.
Conclusion: which business process this improves
RAG over product cards, policies, and FAQs improves request handling and content preparation: 40–50% of routine questions are closed automatically and with a source, freeing operators and content managers from repetitive work.
The key to a sustainable result is architecture. In KT.Team's approach, such an assistant is built as a loosely coupled component: a separate search-and-generation service on top of existing systems (PIM/catalog, helpdesk, guidelines base), not yet another monolith with all the content hard-wired in. This yields two measurable effects. The first — transferability: the index, sources and prompting logic are described explicitly, so the solution can be handed between teams or contractors without losing context. The second — locality of changes: updating product cards or the return policy doesn't break support, because RAG reads them from the source instead of storing a copy. DORA and SRE practices (observability on the share of closed requests, source versioning, a small team of 3–7 people) turn a pilot into a reproducible production process.
