## Data Input

Here, all the loaded datasets are synced into a SQL connection. It's necessary to be compatible with the SQL tool. We grouped datasets by theme so each agent only has a limited number of datasets to query.

![Data Input.png](EH9JpM2lXh5e)

## Agents

All the datasets are connected to the various agents through the SQL tool, then the sub-agents are aggregated to the main store assistant agent.

All agents can potentially be queried independently, but only the Store assistant agent will be connected to the final interface to ensure a more efficient process.

![Agents Connect.png](xlxWfQVdPtXh)

## Markdown Model

To evaluate the impact of pricing on demand, we utilize a demand forecasting model that we train specifically for this project. This model will then be queried by the markdown agent.

![Markdown Model.png](UwxksLkSctYg)

## Prebuilt questions

You don't have to ask all the questions through the Agent Connect interface. If you have repeated questions, you can add them to the [questions](dataset:questions) dataset and use a time trigger to build the responses.

![Prebuilt Question.png](Ex4XEMKABIJl)
