what and why

README.md

@@ -14,7 +14,30 @@
   <a href="https://github.com/Wyvern-AI/wyvern/blob/main/LICENSE"><img src="https://badgen.net/badge/License/Elv2/green?icon=github"/></a>
 </div>
 
-## Check Out [Wyvern's Official Doc](https://docs.wyvern.ai)
+## What is Wyvern?
+
+Wyvern is a real-time machine learning platform for marketplaces:
+
+- **Search and Discovery**: Wyvern specializes in bringing use cases like **recommendations and rankings** in-house.
+- **Empower the Data Team**: Wyvern is tailored for your data team to independently build and deploy production-grade machine learning pipelines for the e-commerce and marketplace industry, reducing the engineering involvement in the entire process.
+- **Orchestration for ML Pipelines**: Wyvern is agnostic to the solutions your pick for your feature sotre, model serving solution, or data warehouse. It automates the process of retrieving data from the feature store and passing data to the model service, as well as logging all the events. It abstracts all the engineering work above away from data scientists, with the goal of enabling data scientists to own the full ML stack, so that they can just focus on defining the request/response schemas of the API, the model, the features the model depends on, the business logic after the model, and finally training the models with the feedback data generated by the ML pipeline.
+
+Our expalation of why we built Wyvern can be found [here](https://docs.wyvern.ai/why).
+
+### Wyvern Architecture
+
+![Wyvern Architecture](/docs/wyvern_architecture.png)
+
+Overall, Wyvern gives you a framework to quickly define your real-time ML pipeline. There are a couple of important components as you can see in the architecture:
+
+1. **Retrieval**: Wyvern can connect to and retrieve objects from your search index.
+2. **Feature Module**: Wyvern has the built-in support for [feast](https://feast.dev/), an open source feature store. It also supports connecting to the feature store that you would like to use. Moreover, Wyvern provides interfaces for you to define your [batch features](/batch_feature) and [real-time features](/realtime_feature) easily, with the support of feature grouping, feature sharing, features for composite entities and request based features.
+3. **Model Module**: Wyvern provides [the model interface](/model_service#define-the-whole-model) that allows you to define your own model in place or call your model service. It provides an interface to define features that your model depends on easily.
+4. **Business Logic**: Wyvern makes defining your business logic easy after the model inference. For example, if your want to promote a specific brand of tshirt and move it to the top of the ranking result for the "tshirt" query.
+5. **Observability and Event Logging**: All the events in your ML application, including real-time feature, model, business logic, product impression, as well your own custom events, are being automatically logged by Wyvern and data can be piped to your data warehouse. Refer to [Logging Events](/logging_events) for more information.
+6. **Training Dataset**: Wyvern provides the feature store serving solution (currently integrated only with feast) to serve all data of the historical batch features and the real-time features that are logged in your data warehouse.
+
+As Wyvern is open sourced, we will bring in more integrations with different feature stores, model serving solutions, search index for retrieval, observability tools, as well as integrations with more data warehouses.
 
 ## Install Wyvern