AI Search & Recommendations
Help customers find the product they meant.
A fast search box can still return the wrong products. We work on the connection between customer language, catalog attributes and ranking, then measure whether the changes help people find and choose something suitable.
DIRECT ANSWER
Wavect implements and improves search and recommendation systems inside shops and marketplaces. The work covers query evaluation, catalog preparation, retrieval, ranking, recommendations and a controlled rollout with agreed quality measures.
Best for
- Stores with irrelevant or empty search results
- Products that need useful recommendations across a large catalog
Consider another approach
- An internal document assistant or a conversational support chatbot
Separate missing data from poor ranking
Review failed queries alongside catalog records. A search engine cannot filter by a size that the product data never records, and a recommendation cannot show unavailable stock safely. We identify data problems before changing the engine. Product Information Management owns sustained catalog cleanup; this service owns the search and discovery experience built on that data.
Make good results explicit
Assemble representative queries, misspellings, synonyms and combinations of constraints. Merchandisers judge suitable results without seeing which implementation produced them. We compare the existing search with candidate keyword, semantic or hybrid approaches. Availability, category restrictions and commercial rules remain explicit filters, rather than assumptions hidden inside a model’s response.
Choose the decision each recommendation supports
Similar products, compatible accessories and repeat purchases require different logic. We define the placement and the evidence available, including what happens for a new visitor or a new item. Consent and access restrictions shape which behavioral signals can be used. The first release may use transparent rules if the available data cannot support a useful learned model.
Illustrative check: an unavailable item stays excluded
A test query asks for waterproof shoes in a particular size. The candidate system must return relevant, available options, keep an out-of-stock bestseller out of the purchasable results and preserve agreed response-time limits. A fresh catalog update is tested separately. The example defines a checkable behavior; it does not promise a conversion uplift.
Compare quality before switching every visitor
We establish relevance and latency baselines, then plan a small rollout with a fallback to the previous configuration. Product Analytics & Experimentation can add controlled experiments and reliable search-to-action measurement. Changes are assessed against returns, misleading recommendations and support issues as well as clicks. Better engagement alone is not sufficient evidence of a better purchase.
Leave ranking decisions understandable
Deliverables include the query benchmark, index configuration, ranking rules, integration code and update runbook. Your team can inspect why an item is included or excluded and rerun evaluation after a catalog change. A customer-facing AI chatbot may use the same catalog, but conversational support and autonomous actions retain their own scope.
Questions before you start
No. We choose the retrieval approach after measuring your queries and constraints. Existing search infrastructure may already support the needed improvements.
Yes. We first review its configuration and integration limits; replacing the provider is a separate decision supported by the findings.
We can use product attributes and defined fallback rules, then evaluate whether behavioral data adds value as it becomes available.
No fixed uplift is assumed. The engagement defines evaluation criteria and measurement so you can decide whether a change should remain in production.
Review your product search
Share your catalog shape, search platform and examples of searches that disappoint customers. Public examples are enough for the first discussion.