Unlike lexical search, semantic search attempts to understand the intent and contextual meaning of a query. Semantic Search can be a very powerful tool you can use within your SaaS to provide delightful search experiences, augmented with AI. In this tutorial, we will learn **how to add semantic search to your Makerkit SaaS using Supabase Vector**. Our use-case consists in providing an enhanced search experience and automatic summarization for feedback submissions from users. ## What is Supabase Vector? Supabase Vector is a set of utilities and practices released by the [Supabase team](https://supabase.com) to make it easier to build AI applications on top of Supabase. As you may know, Supabase uses Postgres to power their database: thanks to Postgres extensions, more specifically PgVector, **Supabase is a prime candidate to build AI applications with**. ### Semantic search use cases What can we build that makes use of semantic search? Here are a few examples: - enhanced search experiences (for text, images, etc.) - customer support chatbots - product search and recommendations - content moderation - automatic tagging - and more! ## Final Result Here is the final result of what we will build in this tutorial. ### The Feedback Submission Form The Makerkit Popup plugin (which this article led to the creation of) allows you to collect feedback from your users. Here is a small demo:

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Adding AI capabilities to your Next.js SaaS with Supabase and HuggingFace