E-commerce & AI
Help shoppers find suitable products with semantic search
Customers may describe a use or constraint rather than an exact product name. Semantic search can connect that intention with catalogue items, but depends on reliable information and explicit selection rules.
Collect customer language and intentions
Gather available searches and common enquiries. Separate exact references, categories and use-based requests.
Create examples with expected results, including misspellings, synonyms and ambiguity. Identify differences between internal catalogue language and customer wording.
Prepare decision-relevant attributes
Specify dimensions, materials, compatibility or customisation as relevant. Missing information cannot be reliably inferred by the search engine.
Maintain changing prices and availability with an authoritative source and known update delay. Good intent matching cannot compensate for inaccurate product information.
Combine relevance with explicit constraints
Text matching, semantic retrieval and structured filters can coexist. Exact product codes and descriptive requests may need different ranking approaches.
Keep mandatory constraints verifiable. For unclear needs, questions or visible filters may work better than confident but unsuitable recommendations.
Evaluate the result lists
Have product specialists review early results, required exclusions and failed searches. Compare equal examples with the previous behaviour.
Keep marketing promotion distinct from relevance rules so the experience remains coherent for shoppers and understandable to administrators.
Improve from observed journeys
Review failed searches, reformulations and relevant product reading under the chosen measurement arrangement. Clicks alone do not prove that needs were met.
Pilot one product family with reference questions, update procedures and fallback. A custom integration can improve conventional search without requiring a chatbot.
Your checklist
- Collect questions and expected results.
- Improve attributes and variants.
- Retain mandatory filters.
- Review relevance with product specialists.
- Monitor failures and reformulations.
Frequently asked questions
Must search become a chatbot?
No. Semantic retrieval can improve an ordinary search field and results list.
Can AI fill missing specifications?
It can help prepare material, but published characteristics need verification. Never invent dimensions or compatibility to improve matching.
A project to explore
Blagapro
Blagapro’s catalogue and customisation options illustrate why reliable attributes matter when directing buyers to suitable products.
Explore the project