Edits and internal linking.

apps/www/_customers/mendableai.mdx

@@ -34,8 +34,9 @@ about: Mendable is Chat Powered Search for Documentation.
 
 Mendable was experiencing tremendous success, growing Weekly Active Users nearly 300% since March. They needed a tool to store and search through large amounts of vector data to improve the efficiency and accuracy of their similarity search operations. They tried Faiss, Weaviate, and Pinecone, but found them to be expensive and not very intuitive, especially when it came to storing metadata along with the vectors.
 
-Why they chose Supabase:
-Mendable discovered that Supabase supports pgvector and found it to be a simple and cost-effective solution. They were impressed with the open source nature of Supabase, as well as its ability to store metadata alongside the vectors. They also appreciated the intuitive interface and ease of use.
+## Why they chose Supabase
+
+Mendable discovered that Supabase supports [pgvector](https://supabase.com/docs/guides/database/extensions/pgvector) and found it to be a simple and cost-effective solution. They were impressed with the open source nature of Supabase, as well as its ability to store metadata alongside the vectors. They also appreciated the intuitive interface and ease of use.
 
 <Quote img="caleb-peffer.jpg" caption="Caleb Peffer - CEO, Mendable">
   We tried other vector databases - we tried Faiss, we tried Weaviate, we tried Pinecone. We found
@@ -45,18 +46,18 @@ Mendable discovered that Supabase supports pgvector and found it to be a simple
 
 ## What They Built
 
-Using Supabase and pgvector, Mendable was able to build a more efficient and accurate search function for their AI chatbot. By storing vector data alongside metadata in Supabase, Mendable was able to quickly and easily search through their customers documentation to find the most relevant responses to queries. They found that Supabase's solution was just as performant as dedicated vector databases, but without the high cost.
+Using [Supabase Vector](https://supabase.com/vector), Mendable was able to build a more efficient and accurate search function for their AI chatbot. By storing vector data alongside metadata in Supabase, Mendable was able to quickly and easily search through their customers documentation to find the most relevant responses to queries. They found that Supabase's solution was just as performant as dedicated vector databases, but without the high cost.
 
 ## The Results
 
 Thanks to Supabase and pgvector, Mendable was able to significantly improve the efficiency and accuracy of their Chat Powered Search for Documentation. They were able to build faster and more cost-effectively using Supabase’s open source stack.
 
-## Tech stack
-
-Mendable's tech stack includes React, Next.js, Express, Vercel, and Supabase.
-
 <Quote img="caleb-peffer.jpg" caption="Caleb Peffer - CEO, Mendable">
   We looked at the alternatives and chose Supabase because it’s open source, it’s simpler, and, for
   all the ways we need use it, Supabase has been just as performant - if not more performant - than
   the other vector databases.
 </Quote>
+
+## Tech stack
+
+Mendable's tech stack includes React, Next.js, Express, Vercel, and Supabase.