See Cube Live: Inside Cube’s AI for FP&A

Video Transcription

I will go ahead and share my screen here and get us started. We're gonna walk through just a couple of slides here before we start diving into the product itself.

But where I really wanted to start off today's session is just kind of by talking about Qube's philosophy around AI and really discussing why AI, why now.

I think all of us can agree that the AI revolution and transformation is here, and it's moving very rapidly.

You know, many of us have already been experimenting in a lot of different AI solutions, whether it's ChatGPT, Claude, some other large language model.

And I think many of us feel very impressed with all of these solutions, but I think especially in the finance space, there's also a little bit of doubt and concern.

This technology is super powerful. It's moving very fast. And in the FP and A and finance space, we also really wanna make sure that we can trust it and trust the numbers that AI is bringing forward to us.

And at Q, we really do believe that AI is going to transform the way that finance teams work.

And our focus really is gonna be around how we can harness this technology to really empower every finance team to become more productive, more effective at their jobs, more confident, but also staying more responsible and secure.

So the way we're going to tackle this here at Qube is by anchoring our AI approaches around three guiding principles, which are going to be Context, Accuracy, and Control.

So starting with Context, the idea of adding Context to AI is that, you know, Qube really sits on a wealth of rich financial data with a lot of information and context behind it, historical data, different financial models, various scenarios.

And our goal here at Qube is to be stewards of all of that information for all of our customers. Now AI is really only valuable when it can understand the context behind your information and easily deliver that context to the users of that solution.

That's really how we're gonna be able to start getting meaningful insights and taking action on those results is if cubes AI can really help teams analyze the past, predict the future, and continue to make better decisions going forward.

Now that second pillar of our AI philosophy here at philosophy here at CUBE is accuracy. Now, of course, we're talking about financial data here. So when it comes to financial data, there's no wiggle room for guesswork or inaccurate information.

So at CUBE, we believe all of the insights that we surface have to be precise, but also really explainable and transparent to our customers.

So we've really rooted accuracy into every AI offering that we have here at Q, and you're gonna see that throughout today's session. Because we understand that you all need to trust your numbers and understand how the AI got there, and that transparency is really a nonnegotiable.

Third, our pillar of control is really around how we take data access and security very seriously here at Qube.

Just like our customers have already been trusting Qube to secure their financial information across their different teams and users, we are gonna be applying that same level of rigor as we start bringing in more AI offerings to our platform.

We wanna make sure that teams have really granular control over who can access what information when down to not only the data, but also the functionality and the insights that AI pulls forward for you.

Now last piece I'll say here around our philosophy is that at Qube, we don't want to just have AI as a bolt on just for the sake of producing an AI offering. We really want to deeply embed AI into our product and embed it into the user experience.

Now our existing Qube customers who are on this session today already know that Qube is a super intuitive platform. And because we're rooted on that foundation of ease of use and intuitive interactivity, that's how the AI offerings are going to feel within our platform as well. They're really built to serve you as you work, be very easy to manage, as well as be very powerful solutions for you.

So with that, I'm really excited to start walking you through how Qube is really starting to bring that AI vision to life across our product.

Now the first feature that we're gonna be talking about today is Qube's smart analysis offering.

Now this feature really roots itself in all of those pillars of context, accuracy, and control, but I think the ones that are really gonna show up during my demo today are gonna be that context and that accuracy pillar.

Now what Smart Analysis is is this is a feature that allows you to automatically flag the top variances across your different data within Qube. So all of your data that's residing in Qube is going to be compiled into this large language model that is then going to generate charts, reports for you that are going to quickly identify those top variances without you having to sift through a bunch of spreadsheets or design a bunch of ad hoc reports.

Now building ad hoc reports is really easy and intuitive within Qb already, But what we've really layered on with this smart analysis feature is the ability to readily break down the drivers of those variances.

So there's easy parameters that you can set to filter these variance analyses down by maybe departments, by entities, product lines, whatever else it might be. But I think the biggest value add here is all of that context that Smart Analysis is giving our customers.

So as you run Smart Analysis within Qube, you are going to receive AI generated narratives and context, explaining your variances with additional context and maybe root cause analysis behind why you might have certain discrepancies across different scenarios.

Now this analysis is really beneficial because you can pull that context out and bring it into a board meeting or share that with other departments who maybe aren't using Qube rather than having to generate that context and come up with it yourself. You can rely on this platform to do that work for you.

And last piece here is that this is also gonna provide the full insight and visibility into the why behind all of your numbers and the results that you're receiving.

So the Smart Analysis feature will enable users to run reports and start drilling into those reports to understand exactly where their variances came from across the different scenarios in their queue.

So with that, we're gonna dig into this feature here directly in the queue platform. So I'm gonna jump out of these slides here for a moment and navigate into the Qube workspace.

And you're gonna see here that on the left hand sidebar, we have this analysis tab where you can start building those smart analysis here. We're gonna click create new, and you'll see this sidebar panel where you can set all of those important parameters to easily add the drivers and the filters that you want to slice and dice this variance by.

So what we're gonna first do is select which scenarios we want to compare in this analysis. I'm gonna run a BVA analysis today, so I'll select my budget. And now I can also set other filters here across all of my cube dimensionality.

Today, maybe I only care about understanding variances in my revenue accounts, so we're gonna filter down to that.

And maybe I only have historical actuals for q one of this year, so I'm also gonna apply a filter for time. Now everything else, I'm gonna leave as is, but I can also set parameters around the variance threshold. So maybe I only care about variances that go above five percent, so we're gonna enter that minimum variance threshold and run that analysis.

Cube is gonna take just a second to render those results, and you're gonna see here that the smart analysis has now pulled up our largest variance by amount. We can see we have about a hundred k variance hitting the platform revenue account in that sales department.

This is in the month of March.

As we scroll down, we're also gonna see these really nice visualizations that Qube has automatically generated for us. So we didn't have to do any legwork upfront to build these. No spreadsheet designs. This is all coming directly out of this AI feature.

You can also hover over these charts if you want a little bit more understanding about these variances. We can see this top one here is about one point two million dollars of platform revenue for the entire quarter.

And as I start scrolling down here, this is where we're getting a lot more of that context into where these numbers are really coming from and what they mean for our business.

This overarching summary tells us that our platform revenue exceeded our budget by about one point three million or thirteen percent, while implementation revenue fell a bit short. So this is showing us some mixed performance across our different product lines in our company, and we can keep exploring these results a bit further.

The primary root cause here is gonna tell us that our strong platform revenue growth was offset by the revenue shortfall, suggesting potential shifts in product mix or potential sales strategies.

We'll also get all of our other important variance highlights down below related to those other key accounts.

But furthermore, in terms of accuracy and really exploring the why behind these results, we can also navigate to this report to drill into these details a bit further.

So this report is gonna highlight for us our top variances here. We can sort those variances by the amount or by the percentage, and we can start digging into the details across our cube dimensions.

So looking at this top one point two million dollars worth of my variance here in platform revenue, maybe I wanna dig into the time periods that generated that. We can see March had the largest driver of that variance. If I wanna explore further, I can dig into the entities that made up that revenue, digging a bit further into products and markets and so on, is gonna get me the most pinpointed results that I can easily digest and then share out with the rest of my team if I need to. So the big idea here is that we're not just delivering numbers with no context or no background as to how we got there. This is giving you the clear picture and the context, the key narratives around what this means for your business so that you can really start acting on this going forward.

And we've already received a lot of great feedback on this feature from our existing customers saying that this has saved them hours of time. They were previously doing a lot of this analysis manually in spreadsheets, but now can rely on this AI offering to really do all that legwork for them.

Now, moving back to these slides, we're gonna cover a couple of other key features here after Smart Analysis.

So beyond just taking your existing data within Qube, digesting it, and delivering insights and results, what we also wanna do here at Qube is help you produce new information and forecast for the future. So that's really what we're gonna be doing here with the smart forecasting feature that we've built into the Qube infrastructure.

What this feature does is it takes all of your existing information in Qube and whatever data you want to feed the model and it runs analysis on that information to generate a new forecast for you with AI.

So oftentimes, our customers will feed in historical actuals or previous forecasts or last year's budget maybe into this large language model, and that model will then analyze that information and give you a new plan automatically.

Now most of the time, you are gonna want to tweak things about this plan going forward or design your own plans, you know, from the bottoms up. But this can be a really nice way for you to just get a starting point on building maybe a new forecast for the next quarter or a new budget for next year. Rather than having to start from the ground up, you can set this as a baseline. And then as you do start to build your plans going forward, this Smart Forecast can kind of serve as a gut check against what your team has put together to make sure that you're on track and trending based off of your historical information.

Now what's really nice about this as well is just like your existing scenarios in Qube, your actuals, budgets, and forecasts, these Smart Forecasts can also be pulled together in ad hoc reports and other templates and models, and you can compare them side by side against your existing scenarios, making this really easy to digest and make sure that you're on the right track across all of your existing plans.

And finally, this is really designed to help you model faster without giving up speed and control. So, ideally, this is going to, apply all of the same existing security settings that you will already have set within your cube. So if you have a user in your cube who can only access the sales department's data, they're only able to generate forecasts for that department.

That also ties back to that smart analysis feature that I was showing before. Anyone who's trying to analyze insights across scenarios can only see the data that you've given them access to. So this really goes back to that control pillar of our AI philosophy here at Qube.

But with that, let's dig in a bit to the demonstration of Smart Forecasting. We're gonna jump back into the Qube work space here to start walking through this flow.

And where smart forecasting is really going to begin is within your scenarios dimension.

Now for those of you who haven't seen demos of q before, your dimensionality here is really how we're architecting your model and constructing all the different ways you'd want to slice and dice your business by. So this is gonna be things like your Chart of Accounts, your Department Hierarchy, but the really key dimension here when it comes to Smart Forecasting is going to be your scenarios.

Now you're gonna have existing scenarios in Qube already, like your historical actuals, your budgets, unlimited iterations of your Forecasts, what if scenarios and other plans.

But now you can take all of that data and start producing new plans for the future without having to do that work manually in spreadsheets.

Now the way we're gonna design a Smart Forecast is by clicking this Create button in the upper right hand corner, and you'll see this Smart Forecast button that we can click on.

Now we can set all of our parameters for what data we want to use to produce this and how far out into the future we wanna build this forecast for.

So what I'm gonna do here is I'm gonna select my Smart Forecast scenario, or I could create a brand new one from scratch today if I wanted to. You can easily overwrite any existing Smart Forecast that you've built before as well if you have new data that you wanna populate there, and you can also set which dimensions you want this to apply to. If I only wanna use AI to build a forecast for my revenue accounts, for example, I can set that filter here, and I can also select the range of time that I want this to span out for. I could make this a rolling twelve months forecast, rolling twenty four months. We're gonna have this go all the way to the end of twenty twenty seven in my case.

And finally, we're gonna select that data that we want to feed into this LLM.

So in my case, we're gonna choose our historical actuals from the past five years or so.

Once we click create smart forecast, this is going to run that analysis behind the scenes. Qub is gonna save all that data to that smart forecast scenario.

And now you can start interacting with that scenario from Qubes spreadsheet applications, whether you're in Excel or in Google Sheets.

I'm gonna open up my Excel model here that I've built to start analyzing my Smart Forecast.

What you're gonna see here in this template is I'm gonna be analyzing data across my different markets, my different product lines for my historical actuals up until May, and we're also going to start pulling in our forecast for those future periods.

But first, let's just fetch down our actuals to see what that looks like. When I click fetch here, just like you've probably seen before in other demos of cube, cube is automatically rendered my data without me having to wrangle a bunch of spreadsheets together. We're getting all of our history starting from the beginning of twenty twenty four out to our current month of historical data, and we've also set up a chart down below to easily analyze that data and visualize that.

But now we wanna see how this actual data is trending against that Smart Forecast that Qube just designed for me. So what I'm gonna do here is from this dropdown, we're gonna pick Smart Forecast and you'll see that in these green highlighted columns, we're now gonna be able to fetch down that forecast that the AI feature produced for me. We are now receiving all of that forecasted data that came out of that large language model. And now as I scroll back down to that chart, you're gonna see that trend.

We'll see that my Smart Forecast is this blue line here, my actuals are the green line, and we can see that Qb has produced a relatively conservative forecast for me. It's not trying to be overly optimistic, because we can see in the last few months that our revenue sort of plateaued a little bit. So we're keeping up with that trend here in that AI generated forecast.

So again, this can be a really great gut check against any existing plans or forecast that your team has put together, telling you whether you need to make things a little bit more conservative or maybe more optimistic in your plans, and this can really save a ton of time in terms of just building new plans from scratch going forward.

But the big idea is that we're taking all of that rich financial data that sits in your cube, and we're using that to start predicting for the future.

Now the third feature that I wanna cover here today is one that's super exciting that we are currently beta testing right now across our customer base, and this is going to be our conversational AI chatbot.

So many of us already use conversational AI, whether it's chatbot is going to be connected to all of your data that lives in your cube.

So we now have an application in both Slack and in Teams that you can connect to your cube model, and you can now start asking plain language questions about your data directly in those conversational tools that you're already using today.

So there's a lot of questions that you can ask. The options are truly unlimited as long as it's related to your data in queue.

But some examples here are things like, why did my q one margins drop? Or maybe how am I trending against my budget?

Or maybe you can ask more suggestive questions like, should I hire new sales reps next month or next quarter based off of my existing data? Is that a smart idea?

And this is connected to a large language model that's going to digest all of your information that lives in Qube to give you the best results possible.

But beyond just generating results in a number or a very brief response, this is also rooted in those pillars of context and accuracy.

So what Qub is going to do is produce all of the details behind how it got to that result, including the actual data that it fetched, the filters and parameters that it set. And just like you saw in that smart analysis feature that we looked at earlier, this will also give you context into what these numbers mean and how you should potentially act going forward.

So this is really helping teams who maybe have a lot of operational users who aren't super comfortable building models in spreadsheets or in a new interface.

They can work exactly in the space that they're comfortable with, Slack or Teams, ask questions just like they normally would about their data, but now they're getting that power of the cube cloud and an AI model to give them accurate responses.

So to walk you through this here, I have an example set up within Teams today. If you were on the previous AI webinar that Suresh and I were hosting, you may have saw this analysis run-in Slack, but I wanted to make sure to show this off for our Teams users as well today.

So when I asked this AI chatbot was, how did I trend against budgeted revenue last year?

So it took just a second or two to analyze that data, and it gave me a lot of information here that I can start to digest.

It first told me the question that I asked. It restated it to make sure that it was accurately understanding.

And it then gave me all of the digestible data here telling me that first, my platform revenue outperformed my budget. We had really strong performance observed in q four that indicated successful execution of our growth strategy.

This was likely driven by new customer acquisition or effective upselling.

We can also see that we maybe underperformed a little bit in terms of our implementation revenue.

So maybe we had some delivery capacity constraints. Maybe we don't have enough implementation managers at this company.

Maybe we need to shift how our services are being sold.

And third, it also told me that our support revenue exceeded budget. So we have pretty strong customer retention here, and we have successful expansion of service contracts as well.

So then it gave me a really nice, detailed context telling me that my mixed performance shows strength in my core platform, but maybe we have a bit of weaknesses in our implementation.

But aside from just giving me this narrative summary, this is also giving me all of the data that I can now analyze on my own if I want to to confirm that these results are accurate.

So we're gonna see this data table showing me all of the accounts that Qubed fetched across my four quarters from last year. It's also displaying all the filters that it used, my actual scenario. We're looking at my total products, global company, etcetera.

And as I scroll down here as well, we can see that it did the exact same fetch or data pull for my budget scenario. So that's how it's analyzing that information together.

And now what we can do is download that Excel file. We can then start running our analysis in Excel, even using the cube application in Excel to start drilling into the details if we want to or slicing and dicing in different ways.

So again, the big idea here is that teams can work where they're comfortable first, but then get all that accuracy and that control over how they want to go forward with reading these results.

Other questions that I wanted to show off too, were some that require a little bit more calculations versus just side by side comparisons of scenarios.

So what I also asked was, what was my revenue per FTE in April?

It reviewed that request. It then showed me what my total revenue for April was about three point six million. It gave me my total headcount, which was about ninety four FTEs, and then it gave me that revenue per FTE.

As we look down below, it tells me that we generated about thirty eight thousand in revenue per FTE, which is a strong productivity indicator.

It tells me what this metric is really valuable for as well. And as we scroll down again, we're getting all of that detailed context around how it got those numbers, what it actually used to filter on that information, and then we can even start filtering that going forward directly in this Teams app. So final question I asked here was, can you just filter that information by only sales reps in the US entity? And as we look at that information that this is collected, we can see we have a different revenue number, two point five million, lower head count, fifteen FTEs, and we have about a hundred and seventy k or so dollars in revenue per FTE. And, again, we're gonna see those filters have changed now. We've got a sales department header here in that data table that cube fetched. We've got our revenue account, our FTEs, and we can see that entity has now been filtered on that US entity.

So instead of having to go into a spreadsheet and run this analysis, you can, again, work right where you're comfortable in Slack or in Teams.

And I wanna call out too that this is currently a beta program that we're inviting our customers to. So we will be sending out links for how to sign up to that program, and you can easily start working with this, experimenting, asking questions about your data. And we really welcome all the feedback that our customers have about this feature as well.

Alright.

Well, final piece here that I wanna cover is we've looked at a lot of really exciting features today, but I'm sure everybody is thinking what's on the horizon for Qube? Where are you going to take AI going forward?

And when we think about our product roadmap here at Qube, we really like to align this to what we call the strategic finance pyramid.

What this pyramid is is a really nice visualization of how finance thinks about their different activities and what activities need to happen first to produce a very strategic finance organization.

The bottom layer of that pyramid is going to be data management. Of course, we need to have really clean, constructed data in order to produce good reports, in order to generate really accurate forecasts and ultimately make strategic decisions. And the way that we're thinking about using AI to support data management here at Qube is in a couple of key ways.

We We wanna be able to use AI to make the data integration process a lot smoother within Qb. So let's say you have new accounts within your ERP and you need to map those into Qb before you pull in your data.

Instead of having to do that mapping work manually or setting just kind of an all encompassing rule for mappings, we wanna use AI to perform smart mappings and predict essentially where accounts might potentially fall within your chart of accounts. Or if there's maybe a new entity in your ERP, it can predict that it needs to add that dimension into queue. So really adding in that kind of generative AI agent experience to that mapping process is gonna save teams a lot of time and give them more accurate insights within queue.

We also want to apply AI for things like anomaly detection. So running analysis from prior periods to the current period of data, checking whether there were any large discrepancies, and notifying our customers of that is going to be really important as well.

And we also want to help out with automated error handling. So thinking about things like maybe I missed an accrual in my source system or, you know, maybe this kinda ties back to anomaly detection. Last year, you had fifty transactions in this account. You know, this month, you only have one. Maybe we need to make some adjustments in our source system. So really making sure we're adding AI to our product to help your source data that comes into Qube be as accurate as possible.

Now that second layer of the finance pyramid is going to be reporting and analysis.

So once your data is clean and supported in the cube infrastructure, we wanna help you also start building reports more easily.

So some of the features you saw today already support this, things like smart variance analysis in our cube workspace.

But we also wanna start taking this more into that spreadsheet experience of cube as well. So using maybe an ad hoc analysis agent where you could type out in plain language in the Excel or Google Sheets application, what type of report you want built out, and that agent would then design that report for you, maybe apply formatting on top of that going forward, and really just assist with that report building process.

Although reporting and ad hoc reporting is already really easy within queue, we see this really helping out with customers who maybe don't have as much spreadsheet experience or departments who really aren't as familiar with Excel or with Google Sheets. This can just make that process of onboarding and adoption a lot smoother for them.

The third layer is gonna be planning and modeling. So we saw a bit around how Q can help you take your existing data and create forecasts for the future, but we wanna take this a step further and really start leveraging AI as kind of a junior analyst within our product.

So we want to build data agents that are going to build new scenarios and build new models for you. So let's say you ask this agent, I need a new revenue forecast built out that's going to take these assumptions and incorporate that into this specific logic. Can you design that workbook for me? And this agent can potentially start building that for you without you having to design that from scratch or work off of preset templates that you'd have to adjust.

So this can really serve as kind of an actionable AI agent versus an agent that just digests and produces data for you.

We also would like to incorporate AI into workflow automation.

So we have been building some workflow capabilities within Qube that you can see on mine and Suresh's webinar in a couple of weeks. But we wanna use AI to really help with that workflow process as well, potentially automatically assigning tasks to users, automatically building tasks based off of a general workflow that you've set without admins having to do that work from the ground up.

And lastly, strategic finance, that top of that pyramid where everybody wants to get to in finance organizations, we want to just make your finance teams as comprehensive and as powerful and productive as possible by incorporating things like prescriptive AI, Taking your data, generating insights, and telling you what steps to take forward next is gonna become really important here.

Using AI to help produce visualizations and board deck presentations instead of having to create that yourselves.

And just thinking about Qube overall as being an AI analyst. So we can really start thinking about AI and Qube as becoming this added headcount that you don't have to hire. You don't have to source out and train. It's going to be this new analyst that's essentially already has an understanding of your business. It comprehends your data and can start taking actions and making strategic decisions on your behalf.

So with that being said, I would like to start passing things back to oh, actually, sorry. One more thing to cover. We do have a few resources that we're gonna be sharing after today's webinar with you all. So walking through these, we will send out a link for you to watch our previous Fireside Chat around AI. So that was with myself and our chief product and technology officer, Suresh. We are going to be sharing out that video recording so you can see kind of other perspectives about AI in queue.

We will also be sharing out a link to our customers to apply for that conversational AI beta, so that Slack and Teams application where you can ask questions about your data.

We'll also be sharing out a link for our customers to access the Smart Analysis feature in Qube as well. So this feature requires no setup time. This is just gonna be a link that takes you right to that landing page in your Qube. So you can start trialing that Smart Analysis feature for yourself, running variance analysis across your scenarios, and getting those nice insights.

We will also send out a link for any prospects on this call today to book a custom demo. So you can work with our sales team and our great solutions architect team to really get a super tailored demo that's specific to your business and your use cases.

And finally, stay tuned for our upcoming Fireside Chat. We are gonna be hosting another web another webinar on June twelfth that's gonna be focused around collaboration and AI powered workflows and collaboration in Q. So that again will be hosted by myself and our Chief Product and Technology Officer Suresh, so stay tuned for more information around that.

Now with that, Alyssa, I will pass things back to you here at this point, and we can start walking through some q and a and answering any questions that folks may have had.

Awesome. Thanks so much, Taylor.

We have a lot of questions come in, so I don't think we're gonna be able to get to all of them during the live session today. But we're gonna make sure that every question that came in gets answered and is provided to you in the email that we send with all of the resources that Taylor just mentioned. So don't worry. If you asked a question, we'll be sure to answer it.

So let's see. The first one we have here, is about the smart analysis.

How can you save and or export this analysis?

That's a great question.

So currently, the smart analysis is really just gonna live within your cube workspace, but that is on our horizon and on our roadmap to enable better exporting of that, potentially, you know, linking up that smart analysis capability with PowerPoint or with Google Slides and making it easier to transfer those insights to a board meeting or to different departments in your company. So really great question. That is absolutely something that we're thinking about here at Qube, and we will be sure to keep everyone on this call updated around where we're at in that progress.

Hey. So does this work for prior year actuals comparisons as well?

For smart analysis, right now, it's really targeting scenario specific comparisons. So think actuals versus budget, you know, budget v one versus v two. But that is also something that we are looking to add to that feature. So either looking at prior year analysis, prior period analysis, maybe even other dimensional analysis that we want to compare side by side.

But really great question. That's, again, added features that we're looking to enhance within that smart analysis.

Awesome. And you may have kind of answered this one, but could you do a month over month analysis with the smart analysis?

Yeah. That's a great question. So, again, yeah, that'll be something that will be added, but something that I didn't mention too is that you can perform that analysis today in the Slack or the Teams application as well as in the spreadsheet applications, of course. But in Slack or Teams, you could simply ask, you know, what did I you know, how do my revenue compare from this month to last month or this year to last year, and it'll be able to give you those results.

Awesome.

Next question, also about smart analysis. Would this use the same data source as the dashboards, or is it pulling in directly from the cube data?

Yeah. Great question. So that will use the same data as the dashboards dashboards because the dashboards are also going to be leveraging your cube data. So any data that resides within your cube today, whether it's your financials that you're syncing up, your data from your CRM, HR data, budgets and forecasts, that can all be spun up in the dashboards in the queue workspace, and it could also be analyzed in that smart analysis feature too.

Awesome. Next question. Does the AI feature come with the platform, or is it offered a la carte?

Yeah. Great question. So it kind of depends on the feature itself. So today, the smart forecasting and the smart analysis will be baked into the product itself.

For the Slack and Teams applications, we will offer a limited number of queries in both of those conversational tools. I, I think today it's set at around fifty queries that you can ask for free, and then you can also subscribe to a more enhanced version and additional queries on your subscription within Qube. So if you wanna have, you know, five hundred queries a month or something like that, you can, you know, expand that use case and purchase additional kind of advanced packages for that. But we're happy to have more detailed pricing conversations on this with anybody who's curious about this going forward.

So k. K. I believe this next question came in during the conversational AI portion. It says, is this helpful for nonprofit and class slash department tracking?

Yeah. That's a really great question. So, absolutely, the answer is it's definitely helpful for nonprofits, for, you know, slicing and dicing by classes, departments, if you want to look at ongoing, you know, proposals or projects.

As long as that data resides in your cube and we have, you know, constructed hierarchies, slicing your data by class or by department, you're gonna be able to ask those questions around that information.

And Qub is gonna run off of that model. It's going to spin up those insights and produce whatever you need around those questions.

The goal here is really to our our product has always been designed to suit any industry, whether it's a SaaS company, a nonprofit company, manufacturing.

And we've really brought that philosophy into our AI as well because we wanna make sure that this isn't a product that's just being used and maintained by, you know, one industry or just by engineers or product experts. We want this to really be used across all types of companies.

Awesome.

This next question, I'm not sure exactly what it's referring to, but I'll just ask it as it was submitted. Does this integrate well with QuickBooks Online?

Sure. So if we're talking just Qube in general, absolutely. QuickBooks Online is a very easy connector for us. We have, you know, out of the box connectors with QBO. We can set up API connections to that.

If you've got, you know, multiple companies on QuickBooks, we can consolidate that data all into one model within Qube. And then once we have that data residing in your Qube architecture, you're gonna be able to add all of that AI layering on top of that to start using the smart analysis to get those nice charts and summaries or building smart forecasts off of your actuals and asking all those conversational questions in Slack and Teams.

Some alright. I think we've got time for one more. So what ERPs does Qube integrate with?

Great question. Yeah. Similar to the last question. So Qube is really a source agnostic platform. We can integrate with any system as long as there is an ability to get data out of that system. And we have a lot of different ways of getting connected to various systems. So whether there's tools that have, you know, APIs we can connect to, sometimes we do more custom database integrations for other tools.

Other times, teams may wanna bring in data as just a one off file import if they want to.

So we can integrate with tons of different ERPs, but just to name a handful, QuickBooks Online, NetSuite, Sage Intacct.

We can connect to things like Epicor. We can connect to, you know, SAP. Whatever you have, we'll scope that out with our solutions architects team and make sure we can get you the right solution.

Wonderful.

Alright. Thank you so much, Taylor, for all of the great information today and for answering all these questions.

Everyone else, please keep an eye on your inbox because as we mentioned, we'll be sending over the recording from today's session, the full document containing all of the questions and answers that were submitted, and then all of the resources that Taylor mentioned earlier, including those beta links. So exciting stuff. You won't wanna miss it. We should be sending that out later today.

And with that, thank you everyone so much for joining. We hope you have a great day.

Thanks, everybody.

Bye.

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