diff --git a/README.md b/README.md
index 20ecdbca..ae2e4310 100644
--- a/README.md
+++ b/README.md
@@ -32,9 +32,6 @@
 
 <hr />
 
-
-> ### Checkout our latest [Sadhguru AI app](https://sadhguru-ai.streamlit.app/) built using Embedchain.
-
 ## What is Embedchain?
 
 Embedchain is an Open Source RAG Framework that makes it easy to create and deploy AI apps. At its core, Embedchain follows the design principle of being *"Conventional but Configurable"* to serve both software engineers and machine learning engineers.
diff --git a/docs/api-reference/app/deploy.mdx b/docs/api-reference/app/deploy.mdx
index 58a6bb81..7cb8ff5e 100644
--- a/docs/api-reference/app/deploy.mdx
+++ b/docs/api-reference/app/deploy.mdx
@@ -2,30 +2,4 @@
 title: 🚀 deploy
 ---
 
-Using the `deploy()` method, Embedchain allows developers to easily launch their LLM-powered applications on the [Embedchain Platform](https://app.embedchain.ai). This platform facilitates seamless access to your data's context via a free and user-friendly REST API. Once your pipeline is deployed, you can update your data sources at any time.
-
-The `deploy()` method not only deploys your pipeline but also efficiently manages LLMs, vector databases, embedding models, and data syncing, enabling you to focus on querying, chatting, or searching without the hassle of infrastructure management.
-
-## Usage
-
-```python
-from embedchain import App
-
-# Initialize app
-app = App()
-
-# Add data source
-app.add("https://www.forbes.com/profile/elon-musk")
-
-# Deploy your pipeline to Embedchain Platform
-app.deploy()
-
-# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
-# ec-xxxxxx
-
-# 🛠️ Creating pipeline on the platform...
-# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
-
-# 🛠️ Adding data to your pipeline...
-# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
-```
+The `deploy()` method is currently available on an invitation-only basis. To request access, please submit your information via the provided [Google Form](https://forms.gle/vigN11h7b4Ywat668). We will review your request and respond promptly.
diff --git a/docs/deployment/embedchain_ai.mdx b/docs/deployment/embedchain_ai.mdx
index f72a2173..45f49054 100644
--- a/docs/deployment/embedchain_ai.mdx
+++ b/docs/deployment/embedchain_ai.mdx
@@ -7,29 +7,8 @@ description: 'Deploy your RAG application to embedchain.ai platform'
 
 Embedchain enables developers to deploy their LLM-powered apps in production using the [Embedchain platform](https://app.embedchain.ai). The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
 
-See the example below on how to use the deploy your app (for free):
+Deployment to Embedchain Platform is currently available on an invitation-only basis. To request access, please submit your information via the provided [Google Form](https://forms.gle/vigN11h7b4Ywat668). We will review your request and respond promptly.
 
-```python
-from embedchain import App
-
-# Initialize app
-app = App()
-
-# Add data source
-app.add("https://www.forbes.com/profile/elon-musk")
-
-# Deploy your pipeline to Embedchain Platform
-app.deploy()
-
-# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
-# ec-xxxxxx
-
-# 🛠️ Creating pipeline on the platform...
-# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
-
-# 🛠️ Adding data to your pipeline...
-# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
-```
 
 ## Seeking help?
 
diff --git a/docs/platform/faq.mdx b/docs/platform/faq.mdx
deleted file mode 100644
index 750988c6..00000000
--- a/docs/platform/faq.mdx
+++ /dev/null
@@ -1,3 +0,0 @@
----
-title: 'FAQs'
----
\ No newline at end of file
diff --git a/docs/platform/overview.mdx b/docs/platform/overview.mdx
deleted file mode 100644
index a7bc86dc..00000000
--- a/docs/platform/overview.mdx
+++ /dev/null
@@ -1,3 +0,0 @@
----
-title: 'Overview'
----
\ No newline at end of file
diff --git a/docs/platform/quickstart.mdx b/docs/platform/quickstart.mdx
deleted file mode 100644
index 331cc491..00000000
--- a/docs/platform/quickstart.mdx
+++ /dev/null
@@ -1,3 +0,0 @@
----
-title: 'Quickstart'
----
\ No newline at end of file
diff --git a/docs/platform/roadmap.mdx b/docs/platform/roadmap.mdx
deleted file mode 100644
index e84a0c2e..00000000
--- a/docs/platform/roadmap.mdx
+++ /dev/null
@@ -1,3 +0,0 @@
----
-title: 'Roadmap'
----
\ No newline at end of file
diff --git a/docs/platform/security.mdx b/docs/platform/security.mdx
deleted file mode 100644
index 3da6f817..00000000
--- a/docs/platform/security.mdx
+++ /dev/null
@@ -1,3 +0,0 @@
----
-title: 'Security'
----
\ No newline at end of file
diff --git a/embedchain/app.py b/embedchain/app.py
index e2f931ca..966f5b6e 100644
--- a/embedchain/app.py
+++ b/embedchain/app.py
@@ -11,9 +11,14 @@ import requests
 import yaml
 from tqdm import tqdm
 
-from embedchain.cache import (Config, ExactMatchEvaluation,
-                              SearchDistanceEvaluation, cache,
-                              gptcache_data_manager, gptcache_pre_function)
+from embedchain.cache import (
+    Config,
+    ExactMatchEvaluation,
+    SearchDistanceEvaluation,
+    cache,
+    gptcache_data_manager,
+    gptcache_pre_function,
+)
 from embedchain.client import Client
 from embedchain.config import AppConfig, CacheConfig, ChunkerConfig
 from embedchain.constants import SQLITE_PATH
@@ -21,8 +26,7 @@ from embedchain.embedchain import EmbedChain
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.embedder.openai import OpenAIEmbedder
 from embedchain.evaluation.base import BaseMetric
-from embedchain.evaluation.metrics import (AnswerRelevance, ContextRelevance,
-                                           Groundedness)
+from embedchain.evaluation.metrics import AnswerRelevance, ContextRelevance, Groundedness
 from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm