How to Build Advanced AI Ecommerce Agents using Flowise, Postgres and N8N (E2B) | Part 2

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In part one of this Episode we built an AI agent for our ecommerce website chat that could recommend products to customers. However now it's time to talk about how our agent can also help us analyze ecommerce sales data to produce charts and graphs using E2B sandboxes.

In order to test our agent, we built a custom database table with over 75,000 simulated sales transactions over 10 months that included all of the products in our ecommerce database so we could plot different charts and graphs to better understand our sales performance.

What's interesting is that even I did not know what the sales data contained before asking the agent.
For example each month had different sales performance with different products in the top ten bestseller lists as well as total sales for the month.

Of course this is not just limited to sales data. Using the E2B Code interpreter and SQL Queries, you can take virtually any data and create interesting charts and graphs as well as do data analysis depending on what you want to do.

We also were able to take our CSV files and do the same thing with Airtable, Flowise and E2B.
While it''s not quite as powerful as SQL you can create charts and graphs and chat with your CSV files without needing to load them into a vector database. Overall it was a great project with lots of exciting applications.

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