Changed Mendable to Firecrawl (#38413)

* Changed Mendable to Firecrawl

* firecrawl logo

* bigger logo

---------

Co-authored-by: Francesco Sansalvadore <f.sansalvadore@gmail.com>

apps/docs/content/guides/ai.mdx

@@ -139,10 +139,10 @@ export const integrations = [
       href: 'https://supabase.com/customers/berriai',
     },
     {
-      name: 'Mendable switches from Pinecone to Supabase for PostgreSQL vector embeddings',
+      name: 'Firecrawl switches from Pinecone to Supabase for PostgreSQL vector embeddings',
       description:
-        'How Mendable boosts efficiency and accuracy of chat powered search for documentation using Supabase with pgvector',
-      href: 'https://supabase.com/customers/mendableai',
+        'How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase with pgvector',
+      href: 'https://supabase.com/customers/firecrawl',
     },
     {
       name: 'Markprompt: GDPR-Compliant AI Chatbots for Docs and Websites',

apps/www/_blog/2023-05-09-supabase-beta-update-april-2023.mdx

@@ -95,13 +95,13 @@ The community is loving `pgvector` to build AI apps so we decided to make it par
 
 [See all the submissions](https://www.madewithsupabase.com/launch-week-7)
 
-## Mendable.ai switches from Pinecone to Supabase for PostgreSQL vector embeddings.
+## Firecrawl switches from Pinecone to Supabase for PostgreSQL vector embeddings.
 
-![Mendable logo](/images/blog/2023-05-09-beta-update-april/customer-stories-mendable.png)
+![Firecrawl logo](/images/blog/2023-05-09-beta-update-april/customer-stories-firecrawl.png)
 
-With Supabase's pg_vector, Mendable.ai could build a more cost-effective solution that is just as performant - if not more performant - than other vector databases.
+With Supabase's pg_vector, Firecrawl could build a more cost-effective solution that is just as performant - if not more performant - than other vector databases.
 
-[Read the full story](https://supabase.com/customers/mendableai)
+[Read the full story](https://supabase.com/customers/firecrawl)
 
 ## From the community
 

apps/www/_customers/firecrawl.mdx

@@ -0,0 +1,78 @@
+---
+name: Firecrawl
+title: Firecrawl switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.
+# Use meta_title to add a custom meta title. Otherwise it defaults to '{name} | Supabase Customer Stories':
+meta_title: Firecrawl switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.
+description: How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.
+# Use meta_description to add a custom meta description. Otherwise it defaults to {description}:
+meta_description: How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.
+author: paul_copplestone
+author_title: Supabase
+author_url: https://github.com/kiwicopple
+author_image_url: https://avatars2.githubusercontent.com/u/10214025?s=400&u=c6775be2ae667e2acae3ccd347fed62bb3f5b3e7&v=4
+logo: /images/customers/logos/firecrawl.png
+logo_inverse: /images/customers/logos/light/firecrawl.png
+og_image: /images/customers/og/firecrawl.jpg
+tags:
+  - supabase
+date: '2023-05-05'
+company_url: 'https://firecrawl.dev/'
+stats:
+  [
+    { stat: '00,000', label: Example stat },
+    { stat: '00,000', label: Example stat },
+    { stat: '00,000', label: Example stat },
+  ]
+misc: [{ label: 'Backed by', text: 'Y Combinator' }]
+about: Firecrawl is Chat Powered Search for Documentation.
+# "healthcare" | "fintech" | "ecommerce" | "education" | "gaming" | "media" | "real-estate" | "saas" | "social" | "analytics" | "ai" | "developer-tools"
+industry: ['ai', 'saas', 'developer-tools']
+# "startup" | "enterprise" | "indie_dev"
+company_size: 'startup'
+# "Asia" | "Europe" | "North America" | "South America" | "Africa" | "Oceania"
+region: 'North America'
+# "database" | "auth" | "storage" | "realtime" | "functions" | "vector"
+supabase_products: ['database', 'vector']
+---
+
+[Firecrawl](http://firecrawl.dev/) provides a chat-powered search engine for technical documentation. Their AI-powered search tool makes it easier for developers and other technical users to find relevant information in complex documentation. Users can simply ask questions in natural language, and the tool returns the most relevant answers. Firecrawl's search engine also provides detailed analytics, which helps teams identify knowledge gaps and areas for improvement in their documentation. Firecrawl has integrated with some of the largest open source projects in the space such as LangChain and LlamaIndex.
+
+## The Challenge
+
+Firecrawl was experiencing tremendous success, growing Weekly Active Users by 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
+
+Firecrawl lear 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, Firecrawl">
+  We tried other vector databases - we tried Faiss, we tried Weaviate, we tried Pinecone. We found
+  them to be incredibly expensive and not very intuitive. If you're just doing vector search they're
+  great, but if you need to store a bunch of metadata that becomes a huge pain.
+</Quote>
+
+## What They Built
+
+Using [Supabase Vector](https://supabase.com/vector), Firecrawl was able to build a more efficient and accurate search function for their AI chatbot. By storing vector data alongside metadata in Supabase, Firecrawl 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.
+
+![Firecrawl - build AI chat search applications](/images/customers/firecrawl/firecrawl.png)
+
+## The Results
+
+Thanks to Supabase Vector, Firecrawl 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.
+
+<Quote img="caleb-peffer.jpg" caption="Caleb Peffer - CEO, Firecrawl">
+  We looked at the alternatives and chose Supabase because it's open source, it's simpler, and, for
+  all the ways we need to use it, Supabase has been just as performant - if not more performant -
+  than the other vector databases.
+</Quote>
+
+To learn more about how Supabase Vector can help you store vector embeddings at scale and build AI apps with ease, [reach out to us](https://forms.supabase.com/enterprise).
+
+## Tech stack
+
+- React
+- Vercel
+- Next.js
+- Express
+- Supabase

apps/www/_customers/mendableai.mdx

@@ -1,78 +0,0 @@
----
-name: Mendable
-title: Mendable switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.
-# Use meta_title to add a custom meta title. Otherwise it defaults to '{name} | Supabase Customer Stories':
-meta_title: Mendable switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.
-description: How Mendable boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.
-# Use meta_description to add a custom meta description. Otherwise it defaults to {description}:
-meta_description: How Mendable boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.
-author: paul_copplestone
-author_title: Supabase
-author_url: https://github.com/kiwicopple
-author_image_url: https://avatars2.githubusercontent.com/u/10214025?s=400&u=c6775be2ae667e2acae3ccd347fed62bb3f5b3e7&v=4
-logo: /images/customers/logos/mendableai.png
-logo_inverse: /images/customers/logos/light/mendableai.png
-og_image: /images/customers/og/mendable.jpg
-tags:
-  - supabase
-date: '2023-05-05'
-company_url: 'https://mendable.ai/'
-stats:
-  [
-    { stat: '00,000', label: Example stat },
-    { stat: '00,000', label: Example stat },
-    { stat: '00,000', label: Example stat },
-  ]
-misc: [{ label: 'Backed by', text: 'Y Combinator' }]
-about: Mendable is Chat Powered Search for Documentation.
-# "healthcare" | "fintech" | "ecommerce" | "education" | "gaming" | "media" | "real-estate" | "saas" | "social" | "analytics" | "ai" | "developer-tools"
-industry: ['ai', 'saas', 'developer-tools']
-# "startup" | "enterprise" | "indie_dev"
-company_size: 'startup'
-# "Asia" | "Europe" | "North America" | "South America" | "Africa" | "Oceania"
-region: 'North America'
-# "database" | "auth" | "storage" | "realtime" | "functions" | "vector"
-supabase_products: ['database', 'vector']
----
-
-[Mendable](http://mendable.ai/) provides a chat-powered search engine for technical documentation. Their AI-powered search tool makes it easier for developers and other technical users to find relevant information in complex documentation. Users can simply ask questions in natural language, and the tool returns the most relevant answers. Mendable's search engine also provides detailed analytics, which helps teams identify knowledge gaps and areas for improvement in their documentation. Mendable has integrated with some of the largest open source projects in the space such as LangChain and LlamaIndex.
-
-## The Challenge
-
-Mendable was experiencing tremendous success, growing Weekly Active Users by 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 lear 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
-  them to be incredibly expensive and not very intuitive. If you're just doing vector search they're
-  great, but if you need to store a bunch of metadata that becomes a huge pain.
-</Quote>
-
-## What They Built
-
-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.
-
-![Mendable - build AI chat search applications](/images/customers/mendable/mendable.png)
-
-## The Results
-
-Thanks to Supabase Vector, 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.
-
-<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 to use it, Supabase has been just as performant - if not more performant -
-  than the other vector databases.
-</Quote>
-
-To learn more about how Supabase Vector can help you store vector embeddings at scale and build AI apps with ease, [reach out to us](https://forms.supabase.com/enterprise).
-
-## Tech stack
-
-- React
-- Vercel
-- Next.js
-- Express
-- Supabase

apps/www/components/Sections/CommunitySlider.tsx

@@ -46,21 +46,21 @@ const cards: CardInterface[] = [
     role: 'Co-Founder, Markprompt',
     quote:
       'We decided to use Supabase over other specialized vector databases because it enabled us to be GDPR compliant from day one with little effort.',
-    image: vectorImagesDir + 'supabase+mendable.svg',
+    image: vectorImagesDir + 'supabase+firecrawl.svg',
     abstract: 'Markprompt and Supabase - GDPR-Compliant AI Chatbots for Docs and Websites.',
     url: '/customers/markprompt',
   },
   {
     type: 'customer-story',
     avatar: '',
-    customer: 'Mendable',
+    customer: 'Firecrawl',
     author: 'Caleb Peffer',
-    role: 'CEO, Mendable',
+    role: 'CEO, Firecrawl',
     quote:
       'We tried other vector databases - we tried Faiss, we tried Weaviate, we tried Pinecone. We found them to be incredibly expensive and not very intuitive. If you’re just doing vector search they’re great, but if you need to store a bunch of metadata that becomes a huge pain.',
     image: vectorImagesDir + 'supabase+markprompt.svg',
-    abstract: 'Mendable switches from Pinecone to Supabase for PostgreSQL vector embeddings.',
-    url: '/customers/mendable',
+    abstract: 'Firecrawl switches from Pinecone to Supabase for PostgreSQL vector embeddings.',
+    url: '/customers/firecrawl',
   },
   {
     type: 'twitter',

apps/www/data/CustomerStories.ts

@@ -305,15 +305,15 @@ export const data: CustomerStoryType[] = [
   },
   {
     type: 'Customer Story',
-    title: 'Mendable.ai switches from Pinecone to Supabase for PostgreSQL vector embeddings',
+    title: 'Firecrawl switches from Pinecone to Supabase for PostgreSQL vector embeddings',
     description:
-      'How Mendable.ai boosts efficiency and accuracy of chat powered search for documentation using Supabase with pg_vector',
-    imgUrl: 'images/customers/logos/mendableai.png',
-    logo: '/images/customers/logos/mendableai.png',
-    logo_inverse: '/images/customers/logos/light/mendableai.png',
-    organization: 'Mendable.ai',
-    url: '/customers/mendableai',
-    path: '/customers/mendableai',
+      'How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase with pg_vector',
+    imgUrl: 'images/customers/logos/firecrawl.png',
+    logo: '/images/customers/logos/firecrawl.png',
+    logo_inverse: '/images/customers/logos/light/firecrawl.png',
+    organization: 'Firecrawl.dev',
+    url: '/customers/firecrawl',
+    path: '/customers/firecrawl',
     postMeta: {
       name: 'Paul Copplestone',
       avatarUrl: 'https://avatars0.githubusercontent.com/u/10214025?v=4',

apps/www/data/ga.tsx

@@ -176,7 +176,7 @@ In the past year, we've had 12 companies start on Supabase and grow from zero to
 
 ![top user growth over time by days after launch](/images/ga/project-user-growth--dark.svg?v=3)
 
-Most of these were AI companies, like Udio, Krea, Humata, Chatbase, Pika, Quivr, Mendable, Markprompt and [MDN search](https://developer.mozilla.org/en-US/blog/introducing-ai-help/) by Mozilla.
+Most of these were AI companies, like Udio, Krea, Humata, Chatbase, Pika, Quivr, Firecrawl, Markprompt and [MDN search](https://developer.mozilla.org/en-US/blog/introducing-ai-help/) by Mozilla.
 
 Postgres has been instrumental in our scalability and adoption. It's versatility is best demonstrated by pgvector: we were the first cloud provider to offer it, and today 15% of all new Supabase projects use pgvector for AI and ML workloads. Look out for a few related announcements this week.
 `,

apps/www/data/products/modules/vector.tsx

@@ -319,14 +319,14 @@ docs.query(
       {
         type: 'customer-story',
         avatar: '',
-        customer: 'mendableai',
+        customer: 'firecrawl',
         author: 'Caleb Peffer',
-        role: 'CEO at Mendable',
+        role: 'CEO at Firecrawl',
         quote:
           'We tried other vector databases - we tried Faiss, we tried Weaviate, we tried Pinecone. If you’re just doing vector search they’re great, but if you need to store a bunch of metadata that becomes a huge pain.',
-        image: '/images/customers/logos/mendableai.png',
-        abstract: 'Mendable switches from Pinecone to Supabase for PostgreSQL vector embeddings.',
-        url: '/customers/mendableai',
+        image: '/images/customers/logos/firecrawl.png',
+        abstract: 'Firecrawl switches from Pinecone to Supabase for PostgreSQL vector embeddings.',
+        url: '/customers/firecrawl',
       },
     ],
   },

apps/www/lib/redirects.js

@@ -2125,6 +2125,11 @@ module.exports = [
     source: '/blog/case-study-happyteams',
     destination: '/customers/happyteams',
   },
+  {
+    permanent: true,
+    source: '/customers/mendableai',
+    destination: '/customers/firecrawl',
+  },
   {
     permanent: true,
     source: '/docs/guides/auth/auth-helpers/nextjs-server-components',

apps/www/public/customers-rss.xml

@@ -162,10 +162,10 @@
   <pubDate>Wed, 17 May 2023 00:00:00 -0700</pubDate>
 </item>
 <item>
-  <guid>https://supabase.com/customers/mendableai</guid>
-  <title>Mendable switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.</title>
-  <link>https://supabase.com/customers/mendableai</link>
-  <description>How Mendable boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.</description>
+  <guid>https://supabase.com/customers/firecrawl</guid>
+  <title>Firecrawl switches from Pinecone to Supabase Vector for PostgreSQL vector embeddings.</title>
+  <link>https://supabase.com/customers/firecrawl</link>
+  <description>How Firecrawl boosts efficiency and accuracy of chat powered search for documentation using Supabase Vector.</description>
   <pubDate>Fri, 05 May 2023 00:00:00 -0700</pubDate>
 </item>
 <item>

apps/www/public/images/product/vector/community/supabase+firecrawl.svg

@@ -0,0 +1,14 @@
+<svg width="206" height="39" viewBox="0 0 206 39" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path fill-rule="evenodd" clip-rule="evenodd" d="M20.0851 6.52001C20.0671 5.36101 18.5631 4.86301 17.8211 5.77201L6.62811 19.479C5.30611 21.096 6.49111 23.483 8.61611 23.483H20.1921L20.3281 32.315C20.3461 33.475 21.8501 33.973 22.5921 33.065L33.7861 19.358C35.1061 17.74 33.9221 15.354 31.7971 15.354H20.1451L20.0851 6.52001Z" fill="#707070"/>
+<path d="M48.7031 19.5H55.3041M52.0041 16.2V22.7999" stroke="#707070"/>
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+<path d="M203.097 30.9377V12.1432H206V30.9377H203.097Z" fill="#707070"/>
+</svg>

apps/www/public/sitemap_www.xml

@@ -2229,7 +2229,7 @@
   </url>
 
   <url>
-    <loc>https://supabase.com/customers/mendableai</loc>
+    <loc>https://supabase.com/customers/firecrawl</loc>
     <changefreq>weekly</changefreq>
     <priority>0.5</priority>
   </url>