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docs/index.md

@@ -26,10 +26,11 @@ Applying AI is becoming increasingly easier but many case studies have shown tha
 
 - [x] Save prediction traces of each prediction including arguments and model versions
 - [x] Save feedback and merge it into a ground-truth database
-- [x] Version and store models and data on shared infrastructure *(MongoDB GridFS, S3-compatible storage, shared local-volume)*
+- [x] Version and store models and data on shared infrastructure *(MongoDB GridFS, S3-compatible storage, shared volume)*
 - [x] Automatically scaffolded custom REST API (and OpenAPI schema) for easy integration
 - [x] Input validation
 - [x] Sensible cache-policy
+- [x] Graceful error handling
 - [x] Seamless support for both synchronous and asynchronous inference methods
 - [x] Easy integration with remote GreatAI instances
 - [x] Built-in parallelisation (with support for multiprocessing, async, and mixed modes) for batch processing
@@ -39,7 +40,7 @@ Applying AI is becoming increasingly easier but many case studies have shown tha
 - [x] Auto-reload for development
 - [x] Docker support for deployment
 - [x] Deployable Jupyter Notebooks
-- [x] Dashboard for high-level overview and analysing traces
+- [x] Dashboard for online monitoring and analysing traces
 
 ## Roadmap
 
@@ -80,7 +81,7 @@ great-ai demo.py
 ![demo screen capture](media/demo.gif){ loading=lazy }
 
 !!! success
-    Your GreatAI service is ready for production use. Many of the [SE4ML best-practices](https://se-ml.github.io){ target=_blank } are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.
+    Your GreatAI service is ready for production use. Many of the [SE4ML best practices](https://se-ml.github.io){ target=_blank } are configured and implemented automatically. To have full control over your service and to understand what else you might need to do in your use case, continue reading this documentation.
 
 ## Why is this GREAT?
 
@@ -98,7 +99,7 @@ GreatAI fits between the prototype and deployment phases of your (or your organi
 
 There are other, existing solutions aiming to facilitate this phase. [Amazon SageMaker](https://aws.amazon.com/sagemaker){ target=_blank } and [Seldon Core](https://www.seldon.io/solutions/open-source-projects/core){ target=_blank } provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great.
 
-However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. ==GreatAI is designed to be as simple to use as possible.== Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the [SE4ML best-practices](https://se-ml.github.io){ target=_blank }, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.
+However, research indicates that professionals rarely use them. This may be due to their inherent setup and operating complexity. ==GreatAI is designed to be as simple to use as possible.== Its clear, high-level API and sensible default configuration makes it extremely easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the [SE4ML best practices](https://se-ml.github.io){ target=_blank }, and thus, can meaningfully improve your deployment without requiring prohibitively large effort.
 
 
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