Readme update

README.md

@@ -11,16 +11,25 @@ The purpose of the code is to provide customers with easy access to innovative i
 
 Please see TAGGING.md for details on object comments.
 
-## Solutions
-
-| Solution | Description |
-| --- | --- |
-| [Supply Chain Network Optimization](https://github.com/Snowflake-Labs/sfguide-supply-chain-network-optimization/) | Using optimization programming to make least cost fulfillment decisions. |
-| [Solution Installation Wizard](https://github.com/Snowflake-Labs/sfguide-solution-installation-wizard/) | A Native App that helps facilitate consumers deploy code from the provider. |
-| [Alert Hub](https://github.com/Snowflake-Labs/sfguide-alert-hub/) | A Streamlit for managing and templatizing Alerts. |
-| [Application Control Framework](https://github.com/Snowflake-Labs/sfguide-application-control-framework/) | A framework that allows for rule-based access and consumer experiences within Native Apps. |
-| [Data Crawler](https://github.com/Snowflake-Labs/sfguide-data-crawler/) | A solution prompting a Cortex Large Language Model (LLM) to generate a natural language description of each table within a database and/or schema. |
-| [Cohort Builder](https://github.com/Snowflake-Labs/sfguide-cohort-builder/) | The Cohort Builder Model leverages Snowflake and Snowpark to create, manage, and schedule cohorts effectively. |
-| [API Enrichment Framework](https://github.com/Snowflake-Labs/sfguide-getting-started-with-api-enrichment-framework/) | A Native App designed to quick start API-driven data enrichment. |
-| [Data Quality Manager](https://github.com/Snowflake-Labs/sfguide-getting-started-with-data-quality-manager/) | A application designed to implement data quality checks, using both Snowflake data metric functions (DMFs) and custom checks. |
-| [ML (Machine Learning) Sidekick](https://github.com/Snowflake-Labs/sfguide-build-and-deploy-snowpark-ml-models-using-streamlit-snowflake-notebooks/) | A no-code app built using Streamlit in Snowflake, designed for building and deploying machine learning models in Snowflake. |
+
+## Solution Types
+A solution can represent one of three types:
+
+* FRAMEWORK - A configurable solution meant for continuous use, augmenting the Snowflake experience
+* HELPER - A typically lightweight utility meant to be used as-needed for specific tasks
+* DEMO - An example meant to be used to understand a topic using synthetic data
+
+
+## Solution List
+
+| Solution | Type | Description |
+| --- | --- | --- |
+| [Supply Chain Network Optimization](https://github.com/Snowflake-Labs/sfguide-supply-chain-network-optimization/) | DEMO | A solution using optimization programming to make least cost fulfillment decisions. |
+| [Solution Installation Wizard](https://github.com/Snowflake-Labs/sfguide-solution-installation-wizard/) | FRAMEWORK | A Native App that helps facilitate consumers deploy code from the provider. |
+| [Alert Hub](https://github.com/Snowflake-Labs/sfguide-alert-hub/) | FRAMEWORK | A Streamlit for managing and templatizing Alerts. |
+| [Application Control Framework](https://github.com/Snowflake-Labs/sfguide-application-control-framework/) | FRAMEWORK | A framework that allows for rule-based access and consumer experiences within Native Apps. |
+| [Data Crawler](https://github.com/Snowflake-Labs/sfguide-data-crawler/) | FRAMEWORK | A solution prompting a Cortex Large Language Model (LLM) to generate a natural language description of each table within a database and/or schema. |
+| [Cohort Builder](https://github.com/Snowflake-Labs/sfguide-cohort-builder/) | FRAMEWORK | The Cohort Builder Model leverages Snowflake and Snowpark to create, manage, and schedule cohorts effectively. |
+| [API Enrichment Framework](https://github.com/Snowflake-Labs/sfguide-getting-started-with-api-enrichment-framework/) | FRAMEWORK | A Native App designed to quick start API-driven data enrichment. |
+| [Data Quality Manager](https://github.com/Snowflake-Labs/sfguide-getting-started-with-data-quality-manager/) | FRAMEWORK | An application designed to implement data quality checks, using both Snowflake data metric functions (DMFs) and custom checks. |
+| [ML (Machine Learning) Sidekick](https://github.com/Snowflake-Labs/sfguide-build-and-deploy-snowpark-ml-models-using-streamlit-snowflake-notebooks/) | FRAMEWORK | A no-code app built using Streamlit in Snowflake, designed for building and deploying machine learning models in Snowflake. |