In this sponsored product demo, the presenter demonstrates how Quadratic, an AI‑native spreadsheet, can replace a traditional, manual Excel workflow that involves logging into multiple tools, exporting CSVs, cleaning data, writing formulas, building charts, and manually summarizing results. The video walks through the entire process: starting a new Quadratic file, using natural‑language prompts to pull live data (e.g., top tech companies’ market caps), applying AI‑generated formulas, creating and customizing visualizations with conditional formatting, and connecting to external data sources such as a Postgres database. The presenter shows how Quadratic can translate plain‑English requests into SQL queries, generate diagnostics, and produce prioritized insights without the user writing code. After analysis, the tool can automatically build a dashboard, adjust chart aesthetics, trace data provenance, schedule recurring runs, and send the finished report and key highlights to a Slack channel. The demo emphasizes the time‑saving, end‑to‑end automation capabilities of Quadratic, ending with a call to try the product for free and links to further AI‑for‑business resources.
Chapters
Up till now, your workflow was probably logging into multiple tools, exporting a bunch of CSVs, cleaning everything, writing Excel formulas, building charts, and manually summarizing the results to send to your team. And if you're doing this weekly or even daily, you know just how tedious it can get. But what if I told you with Quadratic, you can automate these steps entirely? In fact, Quadratic sent this Slack message to my team from a live spreadsheet report that it made and refreshed itself. Their AI-native spreadsheet connects directly with tools like Stripe, Salesforce, or Notion. So once you set up your workflow, you can make it a recurring task and forget about it entirely. If you've watched my videos, you'll know I've recommended Quadratic before, and this time they're kindly sponsoring this video. Let's get into it. First, you want to head over to quadratichq.com, and it's completely free to get started. In here, let's open up a new file so you can get familiar with the interface. At first glance, you'll notice it's quite similar to other spreadsheets, but the key difference is that this one is AI native. While tools like Excel have added AI features later on, this one was built with AI in mind. So as the first prompt here on the left-hand side, we can ask, list the top 10 tech companies by market cap as of June 2026, including Q1 revenue, live stock price, and the most recent news headline about each. So it's been able to pull all of this data from the internet. And suppose we just want to take the sum of the market cap. We can't just use formulas like we might in other spreadsheets here. And we can also format all of this like we would with the commas, as you can see right here. Now suppose that we want to create some kind of a column chart to compare the market caps. So I'm going to say to visualize the market cap with a column chart and an average line. Awesome. So you can see it's created this very sleek average line right here, and that's not really a feature that I see that often in other spreadsheets. From here, we can also ask it to change the colors of the bars dynamically, so some kind of a conditional format here. Where if they're above the average, they should be in a certain color. So that's what it's suggesting over here on the bottom. Let's just say yes and see what it's able to do. Great. So now you can see that the red ones stand out a lot more because they're above average. Also, it's changed the average line from red to black, so we can also still see it in here. And you can easily rearrange this chart, like make it longer. I can also zoom in or out of it if I prefer to. Awesome. Now that you have a better idea of Quadratic, let me show you where it really stands out, and that's with all of its different connectors. And you can connect to virtually anything from your bank account, your broker, all the way to your databases and your messaging apps. If you don't find it here in the catalog, you can always just press on Connect to Anything, and over on the left-hand side, you can just type it in natural language what exact software you want to connect to, and it's going to show you step by step. In my case, suppose I have this dataset right here in Postgres. And just to quickly show you what it is, it's for a fictitious retailer for alpine or outdoor products. You can see here some of the product names, their different categories, all the way to some financials like their unit economics. So instead of having to export this as a CSV file every day or every week and then import it back into Excel or whatever other spreadsheet software, Now all I need to do is go inside Quadratic, press the slash key, and you can see that I have it here as one of my connectors. I've named it Alpine as it's for an outdoor brand. And over on the left-hand side is where you would be able to type in your SQL queries. Now what if you don't actually know how to write any SQL? Well, you can just go over to the left-hand side and type whatever you want in natural language. So I can say to compare the most recent full month of net revenue and profit versus the previous month broken down by country, tell me what's changed and what's driving Saving it, and let's make sure we add the right connector, which is this Alpine one for me. And I'm just going to run that. Let me close out of this part on the side as we're not going to be writing the queries manually. Great. In a few seconds, it's been able to figure out how many rows of data I have in that original database right here. It's also been able to find out the date range, and more importantly, it's found out all of the data. So May is the most recent month. And then April is the month prior. And we can see the breakdown here by country. Even better, on the left-hand side we have a bit more of a breakdown. So you can see overall net revenue jumped, so that's positive. And I did also ask it to break down a bit about what's working and what's not. So you can see right here that the top contributors are some of these countries. The ones that are dragging it or taking it down, it's decided to format in red, which is really nice for these countries right here. I'm using Postgres here, but the bigger picture is that Quadratic isn't limited to CSV files. With their agent sync feature, you can connect to SaaS tools, to APIs, or whatever type of software that your business currently uses. And if you want to test this out right now, you can try Quadratic for free using the link in the description below. Going back to this analysis, now that we understand what's changed, it would be nice to see if there's any trends over time. For that, I can ask for something like show the net revenue by week for the last 12 weeks as a line chart and call out the biggest week-over-week swings. So whether there's been a very high growth or a very big drop, both of those I would like to see if it can show. So it looks like more recently we've had kind of an upward trend, but we have had a big drop over here towards the very end. And we can see a few paragraphs over here with bullets of the full breakdown where we've had huge drops over here towards the end of May, as you can see in this visual. Given that there's so many fluctuations, it might make sense to ask it to scan the entire dataset and try to see if there's anything that needs attention. So I'm saying to scan the data for anything that needs attention— categories with declining revenue, products with thin margins, regions leaning on hard discounts, or revenue concentration list. And overall, I want a short prioritized list for this. So this is by no means an easy task. As I mentioned before, this is 1,600 records And we have several columns in here as well of data. Great, you can see it's created a few different diagnostic tables with these 4 right here, and there's the prioritized list. So it says that 2 categories are in sharp decline, more specifically climbing and apparel. So these are ones that we should definitely look further into. You can see right here the data for climbing and apparel really doesn't look very promising. Then our flagship product seems to be dropping its margin. And if we keep scrolling down, there's not that much concentration risk. So that's not really a problem for us. As you can see, the US is only 35% of revenue. And once we've done this analysis, the next step would be to create some kind of a dashboard or a report to see it visually so we can maybe share it with our team. So I'm saying in a new sheet to create 4 visuals that I think are most relevant for this analysis. Awesome. So these are the 4 visuals, and let's suppose that I don't quite like that they're so colorful. We can easily ask for an adjustment there. So I'm just saying that the charts are too colorful and if we can do a few less colors. Great, that's looking a lot better in my opinion. And let's suppose that for some of this data here, like the top 10 customers by revenue for Aiden, this very high number Let's suppose that we are not sure that this is accurate, so we want to investigate it further. What we can do is go to the supporting data that it provides on the side, like the top customers. And for Aiden, which is this very first one, I can always right-click and go over to Trace Value. When I do that, it's basically going to give me a breakdown of where that value is coming from, what kind of query it made to the original database. So here's the actual detailed query where you can see it's grouped by the customer name. That seems to be accurate. It's also done the order, so the highest goes first, and it's a limit or a top 10 in this case. So that's all looking correct. Once we've done all of the tasks, instead of having to redo the whole thing tomorrow or in a week's time, you can actually just schedule the task and automate it entirely. For this, all you need to do is head over to this run area, and then here under scheduled runs, we can choose how often we want this. Under the custom part, I can click on schedule the task. And here I can choose how often I want to do it. In fact, instead of doing this manually like this, I could also just text it in here. Like I could say every Monday at 9 AM Eastern Time, re-execute all of this. Awesome. So it's been able to schedule that for us. And the really powerful thing here is that when the original database updates, it's going to trickle down and all of this analysis is going to be updated based off of the latest data too. So far, without writing any code, we've been able to automate having to log into a database, import all of these CSV files, analyze the datasets, build different visuals, write up some key points of the results. And now I'll show you how to automate sending it to your team, in this case via Slack. For that, over here, I'm going to type something like, when you're done with your run, notify me on Slack in the revenue channel and summarize the key points. Also send me the actual file itself so I can just basically open it up when I receive the message. So if I hop over to Slack here under my revenue channel, you can see that I have a new update from today and it's saying the dashboard run is now complete. It's giving me some of the key trends, the regions, categories, etc. And of course you can customize this message simply by instructing it. And I've also got the file here so I can hop onto Quadratic and have a look at it. As you've seen, using Quadratic, you can spend less time doing the more tedious tasks and actually have your team work on the important business decisions. You can try it out for free using the link in the description below. And as more and more AI tools come into the market, it can be hard to know which ones you should actually use. And for that, you should watch this video over here where I tested over 100 of them and chose the best ones. Or you can check out our AI for Business and Finance course over here. Hit that like and that subscribe, and I'll catch you in the next one.
