AI search on site (semantic search)
We implement semantic AI search on the site: it understands the meaning of the query, not just exact words — it finds what is needed even if the user phrased it differently than the catalog. Less 'nothing found', more finds. Honestly upfront: semantic search is noticeably better than ordinary word search but is NOT perfect — it sometimes returns irrelevant results; quality depends on your data; it does not guarantee sales growth; and the model's work costs money (per token/resources), plus it requires setup and verification, not 'turn it on — and it finds everything perfectly'.
AI search on site (semantic search) — overview
AI (semantic) search is site search that understands the MEANING of the query via embeddings (vector representations — 894) and a vector database (892): it finds the relevant even if the query words do not match the words in the product/article (synonyms, descriptive phrasing, typos). Unlike exact-keyword search, it understands 'a jacket for winter in the mountains' ≈ 'a warm ski down jacket'. Honestly about 'better but not perfect', this is key: semantic search noticeably reduces 'nothing found' and raises relevance but is NOT flawless — it sometimes returns something close in meaning but not what is needed, or gets confused in narrow terminology. This is a normal property, we tune and measure it rather than pass it off as ideal. Honestly about data: search quality depends on the quality and completeness of your data (descriptions, attributes) — searching well over empty/bad cards is impossible. Honestly about 'no sales guarantee': better search raises the chance the user finds what they need (and thus buys), but sales growth is not guaranteed — it depends on the assortment, prices and the whole experience. Honestly about cost: semantic search uses a model/embeddings and infrastructure (a vector DB) — these are resources and costs, including per token; we help pick a solution for the budget. Honestly about setup: it is not a 'box' but a configurable system — indexing, tests on real queries, refinement are needed. Honestly about access: data for indexing and site access are needed. An important boundary: this is search; a knowledge-base assistant (answers with text) — 868; recommendations — 872; vector DB — 892; embeddings — 894. Picture this: instead of 'empty results though the product exists' — search that understands what the person meant. The base price starts from 45,000 ₽ (depends on data volume).
Problems we solve
- Site search does not find a product if the words did not match exactly.
- Frequent 'nothing found' — users leave.
- Synonyms, descriptive queries and typos break search.
- A large catalog, but search over it is weak.
What's included in the AI search on site (semantic search) service
- Semantic search via embeddings (894) and a vector DB (892)
- Understanding meaning: synonyms, descriptions, typos
- Indexing your data and relevance tuning
- Tests on real queries and refinement
- Honest boundaries (not perfect; depends on data; cost)
- Effect assessment on findability
- A link with the knowledge base (868) and recommendations (872)
- Handover and review with you
What you get
- Search understands meaning, not just exact words
- Less 'nothing found', higher findability
- Works with synonyms, descriptions, typos
- Honest boundaries (not ideal; sales growth — not a guarantee)
How the work goes: steps
- We assess data and typical queries; collect access
- We set up embeddings, the vector DB, indexing, relevance
- We test on real queries, honestly set boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Does semantic search always find exactly what is needed?
Noticeably better than ordinary, but not perfect — honestly. It understands meaning and reduces 'nothing found', but sometimes returns something close in meaning rather than exact, or gets confused in narrow terminology. This is a normal property that we tune and measure on real queries. We raise findability, but '100% always the exact thing' cannot be promised.
What does AI search quality depend on?
Above all on the quality and completeness of your data: searching well over empty or bad cards/descriptions is impossible ('garbage in — garbage out'). Also on relevance tuning and tests on your queries. We help put data in order and tune search, but honestly: without proper data even good search is limited.
Will this raise sales?
It raises the chance the user finds what they need and buys, but we honestly do not guarantee sales growth — it depends on the assortment, prices and the whole experience. Good search removes the 'did not find — left' barrier, and that genuinely helps, but it is one factor, not a magic revenue button. We measure the impact on findability honestly.
About the provider
The «AI search on site (semantic search)» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.