Search that helps shoppers find more

Vidisape is a semantic AI search layer for ecommerce catalogs. It helps shoppers express intent naturally and returns relevant products even when their wording does not match your catalog exactly.

Connect your ecommerce platform, let Vidisape index your catalog, then review search behavior in the dashboard. You can tune results with boosts and rewrites while AI-assisted enrichment improves the vocabulary behind each query.

Semantic search connects a shopper's intent with relevant products, not just matching keywords. Keep product titles and descriptions clear, then use analytics to identify gaps in your catalog language.

Vidisape can recognize common misspellings and still guide shoppers toward useful results. Review zero-result queries regularly to identify terms that need a synonym or rewrite.

AI-assisted enrichment helps organize product attributes, synonyms, and tags so your catalog can answer more ways of asking for the same thing. Review generated values before publishing changes.

Boost rules let you give selected categories more visibility for relevant queries. Keep rules specific, document their purpose, and revisit them as your merchandising strategy changes.

Use a rewrite when a known customer phrase needs a deliberate interpretation. A rewrite can map a phrase to a category, synonym, or preferred query without changing the original catalog data.

Similar-product discovery helps shoppers continue browsing from an item they already like. Use clear product data and meaningful attributes to improve the quality of recommendations.

Use query analytics to understand demand, engagement, and gaps. Focus on recurring zero-result searches and queries with low engagement when deciding what to improve next.

Keep customer-facing catalog terms consistent across the languages you support. Vidisape's dashboard labels may remain in English while your product data reflects your storefront language.

Give teammates the access they need for their role. Separate day-to-day search tuning from broader catalog or workspace administration where your setup supports it.

Start in the dashboard to review catalog status, recent search activity, and areas that need attention. Use it as the home base for search quality work.

Filter and review common queries, engagement patterns, and searches that need merchandising attention. Turn repeated findings into focused improvements.

Open zero-result queries, identify the shopper's intent, then add a synonym, rewrite, product, or category that closes the gap.

Create a rule for a clear merchandising goal, choose the relevant category or products, and confirm that it does not hide better results for adjacent intents.

Write the customer phrase and the intended interpretation, then test nearby variations. Keep rewrites concise and easy for teammates to understand.

Review suggested synonyms and tags for accuracy, then publish the ones that reflect how your shoppers actually search.

Maintain accurate product attributes and category structure. Search quality depends on the product data available to the index.