---
title: "AI Product Search & Recommendation Systems"
canonical: https://wavect.io/services/ai-search-recommendations/
language: en
description: "Improve product search and recommendations with catalog evaluation, relevance tuning and measurable rollout. Wavect builds search around real customer queries."
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

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.

THE ENGAGEMENT

## Real queries. Relevant results. Measured changes.

1. **Build a query set** Capture customer vocabulary, difficult searches and intended results.
2. **Tune the experience** Connect catalog attributes, retrieval, ranking and recommendation rules.
3. **Evaluate the change** Check relevance, latency, coverage and business guardrails before rollout.

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

## Questions before you start

### Does this require a vector database?

No. We choose the retrieval approach after measuring your queries and constraints. Existing search infrastructure may already support the needed improvements.

### Can you work with our current search provider?

Yes. We first review its configuration and integration limits; replacing the provider is a separate decision supported by the findings.

### How do you handle new products with no click history?

We can use product attributes and defined fallback rules, then evaluate whether behavioral data adds value as it becomes available.

### Do you guarantee increased sales?

No fixed uplift is assumed. The engagement defines evaluation criteria and measurement so you can decide whether a change should remain in production.

## Choose your next step

- [Repair catalog quality](/services/product-information-management/)
- [Measure search changes](/services/product-analytics-experimentation/)
- [Add conversational product assistance](/services/ai-chatbot-development/)

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