See what's next at Zone: An early look at ZoneLiquidity

Summary

Cash forecasting shouldn't live outside NetSuite. Watch Zone's ZoneUnplugged session for an early look at ZoneLiquidity – AI-powered cash forecasting built directly into NetSuite. 

• See an early look at how ZoneLiquidity brings AI-powered forecasting and scenario planning into NetSuite
• Learn what's new here if you're already a ZoneReconcile customer – and what it means if you're not yet
• Hear directly from the ZoneLiquidity Product Team on what's here today and what's ahead

Transcript

Haley Ashe

Good morning, good evening, or good night, depending on where you're joining us from today. And welcome everyone to zone unplugged.

See what's next, an early look at zone liquidity. So we have a lot of good content in store for this audience today, so we're gonna go ahead and dive on in.

So we're gonna start with just some quick intros to start with myself. Again, welcome everyone.

I'm Hailey Ash. I'm the director of customer marketing here at zone and co. I am the person behind the scenes a lot of times, helping to bring you, our customers, our prospective customers, more resources like Zone Unplugged, our Zone monthly newsletter, and so much more.

I will be your host today along with my friends here from the Zone team, and I'm gonna pass it over to have them introduce themselves. So, Andrea, let's start with you.

Andrea Boyle

Thank you, Haley. Hello, everybody. Thank you for joining us today to learn more about a new product coming to market, ZONE Liquidity.

My name is Andrea Boyle, senior product marketing manager here at ZONE. I've been with ZONE for two years officially now.

I am responsible for all of our procure to pay and treasury solutions. Viraj, I'll hand it over to you.

Viraj Mistry

Hi. My name is Viraj. I'm the director of, director of product for payments and treasury.

Excited to speak with you all today. I've been at Zone for about a year now.

So, yeah, looking forward to presenting what we've what we're bringing to market.

Anita Stamenkovska

Alright. And, I'm Anita.

Super excited to be on this call today. I have been with zone with about almost six year.

I'm the product manager on the ZoneReconcile side and excited to walk you through Liquidity today.

Haley Ashe

Amazing. Thank you, everyone.

And then before we do dive in, we have just a couple quick housekeeping notes for today's session. So, for today's session, the chat has been disabled, but you can submit questions at any time using our q and a feature, and we would definitely encourage you to.

We love interaction, which is why we have carved out a dedicated portion of today's webinar just for q and a. So we'll do our best to get through as many questions as possible during that time.

If you are a current zone customer and have an account specific or support related question, feel free to go ahead and send those through as well, and we'll make sure those get to the right folks afterwards. And we will also be running a few polls during this session, so keep an eye out for those to launch as we go in the right panel as well.

And lastly, this session is being recorded, and you'll receive the recording in the next two to three days via email. And then don't forget, by joining us here today, we have some free money on the table, and who doesn't love a little bit of free money?

So be sure to stay till the end if you can, and that is where we'll draw the winner for today's episode. Alright.

So let's get into what we have in store for today. We have an exciting lineup.

Andrea will give you all a quick introduction to the full zone ecosystem of solutions for anyone joining us today that doesn't know all the things we do here at zone, Followed by Anita giving us an exclusive first look at one of zone's latest solutions, zone liquidity. Then we wanna hear from you.

So we'll jump into a short q and a, then we'll close out with a few zone updates. And finally, we will wrap up by drawing our winner for today's episode of zone unplugged.

So without further ado, let's go ahead and get on into it. So over to you, Andrea.

Andrea Boyle

Thanks so much, Haley. Everyone, if you're not familiar with Zone and Co, we build software that helps finance teams operate more efficiently with NetSuite at the center of their workflows. Our solutions support key finance processes like AP, billing, payroll reporting.

Closest to what we're covering today is bank reconciliation and treasury, helping teams really reduce that manual work, improve visibility, and scale operations with more control. Today, we're specifically focused on treasury, how finance teams can move more from reconciled view of cash with zone reconcile, he already gives you this, to more of a forward looking one with zone liquidity.

We're proud at Zone to support more than 4,500 customers globally and over a decade of experience focused on finance and NetSuite. So with that, let's jump into today's session.

First thing, here we go. We're gonna go into a poll.

Alright. So today, we have a poll for you. Let's start with what are you using today?

What does that look like for you for your cash forecasting? We talk to many different people running finance departments, whether you're a controller, CFO, FP and A.

What are you using? Are you primarily using Excel? Are you using a TMS system?

Are you using NetSuite's own, solutions? Or maybe you have another solution that you built yourself or something else that you're using completely different.

So giving everybody a chance to vote here and looking at these results as they're coming in, it's no surprise at all. Haley, if we can go ahead and stop that.

And looking at the results, right now, we can see majority are using Excel. So that is great to know because that is what our research has shown us already.

So jumping into things, you know, let's ask the real question. When's the last time that your forecast was actually right?

Not close, not directionally fine, actually right the day someone looked at it. I'm guessing most of you already know this pattern because you're living it.

Most of you are using Excel. You pull the numbers from the bank.

You pull them from NetSuite. You rebuild the model again. And then about five minutes, you have a forecast you can't trust.

Then something changes. A customer pays late. A vendor moves a payment date.

Sale closes a deal nobody told finance about. And just like that, your forecast that you spent hours rebuilding is describing a version of the business that doesn't exist anymore.

So it's a loop. Full data. Rebuild the model.

Something changes. Start over and every time through that loop, you're spending real hours, hours you don't have during close to produce a forecast that's already a step behind before you've even finished building it. That's not a discipline problem.

It's not an Excel skills problem. It's a structural problem.

You know, especially if your forecast lives outside the system where your transactionally transactions are happening, your ERP, NetSuite here, It can even you can even get more dis more disconnect in the data. So we asked a we asked a different question.

What if the forecast didn't live outside of NetSuite at all? What if it lived right next to the data and updated itself instead of waiting for you to rebuild it?

That's the gap we're focused on closing right now, and we're gonna start with Viraj. He's gonna talk to us a little bit about zone liquidity.

Viraj Mistry

Perfect. So, you know, I think Andrea has made it very clear what the gap is. The forecast is stale before you've even finished building it, and that's what zone liquidity is here to close.

So what are we trying to do? So zone liquidity is taking your reconciled data, data, data that you already trust in NetSuite.

And we and we're adding to it your open transactions, your AR, your AP, your journal entries, including things like payroll to give you one forward looking view of cash. If you're if you're already reconciling in zone, this isn't like a new system you have to stand up.

It's the next layer on top of what what you already have. And who is it for?

Well, we've built it for people living in this every day, so controllers, VPs of finance, treasuries, cash cash and cash analysts. But I suppose moving on to the next slide, I wanna be really clear about what kind of forecasting this actually is.

So, obviously, this isn't generative AI narrating what's already happened. It's it's building a solution that predicts what's coming.

So it uses your own cash flow patterns. So not using generic models.

It's looking your historic data and upcoming data to try and create models that offer value to the customer. So what do we have today?

That's the AI powered cash forecasting tool across all transaction types on your reconciled NetSuite data. In addition to that, we have a very intelligent working capital insights tool, which allows you to optimize certain payments and also a fantastic scenario studio, scenario planning kit that allows you to query and challenge and adjust any, anything that you have, running in the forecast.

But we're not gonna stop there. We we believe in building out something that genuinely delivers long term value for customers.

At the end of the day, the ambition is to replace the spreadsheet. So we're adding in lots of different sort of more detailed, intricate features around intercompany's custom field intelligence and confidence and variance tracking.

So it will fit your your it fits your business more precisely precisely. And, the direction is simple.

The NetSuite native liquidity solution that replaces the spreadsheet. Hopefully, you'll see it's trusted today, and it's getting smarter and wider from here.

So with that, I will hand over to Anita, to go through the demo.

Anita Stamenkovska

Thank you, Tim. Great introduction.

Now let's take a look at the details. And once again, thank you very much for joining this webinar.

So in the next couple of minutes, I will be walking through zone liquidity's four main functionalities, which are the liquidity, the cash forecast itself, the working capital insights, the scenario planning agent. At the end of the session, we really would like you to walk away with three main takeaways.

Those would be, houses on liquidity can help finance team identify their cash position at any point in time. What this cash position will look like in the upcoming future, and also where account payable and accounts receivable are yielding opportunities or risks for our finance teams.

Now, the beauty of the system is that it fully lives in the system, as our team mentioned prior. The very next challenge when building a cash forecast is always the assembly point, meaning which are the entry points that we need to pull into our cash forecast to have a great output that we can trust.

Now the dataset building with his own liquidity is facilitated by a really simple setup assistant. And the setup assistant consists of a couple of sections in which the first one is the simple general preferences.

Now we can select subsidiaries and also bank accounts. This option is here in all those events when we would like to take a subsidiary out because it has really narrowed or minimal transactional movement, and the same stands for the bank accounts.

When our bank accounts have low transactional movements, we don't want it to be part of the cash forecast and also the opposite. We can build cash forecast for each of our subsidiaries if applicable.

Next is the cash forecast period. Now, we provide a couple of predefolded options for our end users, but a custom date can also be selected.

When the forecast period is defined, the next item is to define the transaction types. Now, the transaction types that you see being available and selected here are the ones that are going to be applicable for the cash forecast and also the working capital insights.

Now as our team mentioned prior, today's cash forecast within this, demo version is built out only based on historic cash payments. We're looking into expanding the accuracy of the cash forecast by ingesting and incorporating account payable and accounts receivable within the cash forecast.

On top of this, apply AI logic to, strengthen out the signals of the, enhanced projections. Now this is a version that is going to be available at the end of September, so please stay tuned with us within our release notes.

When the transaction types are selected, next item are the destination rules. Now, the destination rules consists of liquidity floor and cash milestone target.

The liquidity floor is this tiny functionality that is comprising all expenses we know we have to execute payment for at the month end. These are our non negotiable expenses that we have to cover.

This is the liquidity floor that we need to maintain at our bank balances at any point of time. On the opposite, the cash milestone target practically is the KPI, is the direction which we are aiming to achieve at the end of the forecasted cash period.

Now, these parameters are fully optional. But to simply please know that we do recommend and encourage our users to record them.

The reason is they can later on be a great input for the scenario planning agent, and we are going to fully cover the scenario planning agent and its capabilities a little bit later. As of the setup, building up the cash forecast, this is practically it.

By reviewing and executing, we will be reviewing all the parameters that we have selected in the previous two steps, and from there, sync our transactions to the AI model with the purpose to get the output of the cash flow, cost liquidity, and the working capital insights. Now, we're going to continue with basics.

Why we're saying this? We all know that our cash forecast is as good as fast as our bank accounts are reconciled. Now where liquidity comes into place, it provides a full snapshot for our finance teams to know what the cash position is today.

And we introduced this with three different parameters out of the bank statement balance, the connected balance, and the NetSuite general ledger balance. The difference is there with the purpose so we know whether we need to chase our accounts payable team, accounts receivable team to reconcile the differences or just check those, outstanding, positions.

This is where the module comes, really interesting. The reason is by a simple date switch, Users can look behind, can look at the bank balances within the past, but also do the same for the future.

So let me show you. If we're interested of our cash position at end of q two, end of q one, at the end of 2025 as an example, we can just do this within a simple click. A custom date is applicable as well.

Now I can notice this table is named current bank account balances. What about the future?

Let's take a look. If we select a date in the future, and for this purpose, I'll just select, end of December two thousand twenty six. You'll notice the table is converted to forecasted bank account balances table.

What the system takes into account is the current balance, adding on top the expected calculated inflows and outflows, and from there calculating the forecasted balance. Now, please note, these are just numbers that we display for end users, but end users can also further even drill down as an example.

Just because my cash forecast is only built for the Midland subsidiary, my expected inflows and outflows are applicable for my bank account in this corresponding subsidiary. Meaning, if I click under the expected inflows, I will be having a full insight of the cash events which are going to be part up until the end of December.

The same logic stands for the outflows or the outgoing payments. Now, if we would like to further drill down within these cash events, this is where the cash forecast comes into play.

Now let's take a look. For this purpose, I'll navigate to setup and just simply forecasting. Now as our team indeed said, the cash forecast is based, on an AI model that has capacity to look at the best patterns of our customers at the best patterns of our account payable team discipline, and so on, and apply two type of AI methods.

The first one is the AI method, and the second one is the deterministic one. The difference between these two is that within the deterministic pattern, the AI model was able to identify patterns applicable for a particular customer or a vendor in the past period.

And where AI comes into place is for all those cash events where there was no clear pattern, but the system had enough information to generate a cash event. When there is no enough information, the system is not going to generate a cache event or represent a value, but rather not report simply anything.

Now if we take a look at the chart as an example, you'll just notice that a cash milestone target is represented in a green line, the liquidity floor is there, within the red line. These are the parameters we recorded within the setup assistant.

You'll just simply notice that the system is using two different models, AI prediction and deterministic. Now, just because the cache output is going to be different across each user, the AI prediction and deterministic behaviors sometimes can overlap.

This is fully dependent on the data that we're working on. Now, let's take a look at the detailed cash events.

What is behind this chart? What is behind our cash forecast? Let's take a look at the breakdown.

Now, what the system does, it generates the cash events, it displays them as individual items, but the system also generates a snapshot of the total inflows and total outflows for the upcoming period. Now, let's take a look at example number one.

You can notice there's an outflow, which in our use case is mentioned as other AP without a customer or a vendor with the reference of time GPT. Now, indeed, the system is falling into time GPT time series in all those events when no pattern was identified, but the system had enough information to form a cache event.

The focus lineage is a transparency layer, which is supporting the cash event in the sense of serving as transparency layer. Why this cash event was generated?

What is behind it? We know that we as finance teams are always interested around this information and practically within the finance world, there's indeed no black box problem. Now on the right hand side, let's take a look at the forecast lineage.

The system would be always displaying the methodology, meaning how did the AI model worked out the cash event, which were the confidence factors. You'll just notice the pattern match rate is up to zero.

The pattern details are going to be available, the prediction rate, of course, the source transactions based on which this cash event was generated, and also summary of the upcoming cash events for the coming period because my cash forecast is created for Q4 twenty twenty six. Let's take a look at another example.

Let's take a look at example number two. Now, for this scheduling, the system was able to identify the customer and it's clearly there.

Let's take a look at the transparency layer. Again, the methodology will be available, Confidence factors as well as well.

You'll just notice the pattern match rate is, is increased up to 88% at this time. The system calculates and displays the pattern details.

The source transactions are going to be available, and once again, the upcoming forecast. This is what we call the audit trail of each cash event within our cash forecast.

Now, I'll navigate to the transaction sublist. As a general rule, as a main takeaway, the system is refreshing the data or syncing the data to the AI model by default on a daily basis.

But for example, when we would like to take a look at our cash forecast, we can also sync the data on ad hoc basis just by using refresh forecast. Now, what this actually does, all new transactions that our APTeam, AI Teams created potentially today or in the past week, those are going to be synced within the transaction sublist.

This is what we call the audits trail of transactions that were useful that the AI model practically built our cash forecast against. Finally, what we would be always interested in is the variance analysis.

Now, the system would always be displaying the forecasted amount. The actual value or the actual amount is always going to be refreshed on a daily basis as soon as we are reconciling our bank statements, as soon as payments are created.

From there, the system would have capacity to calculate the variance. Now, this is the functionality that is allowing us to have trust in our cash focused, to have trust in the AI model, and currently not available.

But what we are working on and looking into is for the next release available within September is to go one step further and provide transparency layer for our end users in the sense of when the variance between the forecast and the actual was positive. All negative, what were the parameters that drove that distinction?

We do consider that this is indeed a crucial point so that our users can trust their forecast, have much more transparency in what the model is capable to, to to execute or to generate with them. Now with the scenario planning agent, users can also create a cash forecast.

What this actually mean? Let me show you a couple of examples. Now the scenario planning agent is super powerful.

And, for this scenario, I will not be submitting new new queries, but to show you a couple of examples that we were able to work out as a test just prior this prior this, priority scope. Now let's take a look at the first example.

The first question that we would like to query our cash forecast, and this is how powerful the system is, as an example. Looking in fiscal year 2026, when is the month to date free cash flow at its lowest, and what opportunities do we have to increase the free cash flow for that month?

Now take a look what the system does. It builds up the free cash flow analysis, breaks it down per month.

It is indicating us that indeed in our use case, December is our tiniest month. It lists down the details why, then it also provide opportunities to increase December's free cash flow.

At the end of this section, the system provides a quick summary. Now, let's take a look at another example.

What month does our DSO negatively impacts our free cash flow? Once again, the system is creating our cash forecast.

It is creating a monthly breakdown. Once again, we do know December is problematic for us and these are the key risks.

Once again, the system also provides recommendations in the sense of, would you like me to model a scenario, for example, what a December position could look like if collections are accelerated or if we had the expected January seat backdated into December? Let's take a look at the collection terms.

This is the, example that is covering this use case. What's an aeroplane agent does with this, with this input, it is querying the baseline, of course.

It is listing down December's position. It is building out scenario number a, accelerated collections, working out the details.

Also represents the expected output for scenario number, B, adding the expected January receipt. Further down, it is providing a side by side comparison for the December net position.

Key takeaways are also going to be available within this summary as an output and the cumulative cash position, original versus planned scenario is going to be, available as well. Now what also finance team's, value is to simply I'm new to this company.

I have very little information of the financial breakdown, obtain any feedback from the forecasted bank balances. What should I know?

Right? What actions we need to take, into place just to improve our cash position? Now once again, the system is providing, directions, querying the forecasted overview, q four of two thousand twenty six.

It is listing down the key issues identified. It's working out the details again, and it is, of course, proposing the recommended, recommended actions.

And I would like to finalize my walkthrough by also, walking you through the working capital insights, which are also a supporting functionality to our cash focused in the sense that the system is again taking all our accounts payable and accounts receivable. It is grouping them within header level or total buckets.

The system also shows potential savings in events when we have transactions which have potential discounts and also it is indicating as if we have some critical actions that need our attention. The details are, of course, also important.

We further drill down the data into or categorize the data into payment buckets, accounts payable versus accounts receivable, which follow or share the same logic in the sense of early payment opportunities, urgent payment requires, critical action required, and the same stands for the air side. But once again, in a different context, expected to time and the risk, critical collection.

The system can drill down within these details again, list the transactions which are behind each of the tiles. And, once again, as we said at the beginning, this is your NetSuite account.

This is your data. You can always reach edit transactions just via a NetSuite link. Thank you.

I will stop here.

Haley Ashe

Amazing. Thank you so much, Anita.

That was a fantastic overview. We appreciate you walking us through that. So at this time, if you haven't already, drop some questions in the Q and A for us, because we're we're in our Q and A portion of today's call.

And just a reminder, any questions we cannot get to today, we will be sure to follow-up with, with your account manager or our support team following today's session. But with that said, we're gonna go ahead and get on into it.

So I'm gonna go ahead and cast one of our presubmitted questions, to the stage. So the question was, we already have a forecast built in Excel.

Why should we switch? So, Viroj, do you mind answering that one for us?

Viraj Mistry

I can do. So I think we touched on this in the presentation.

One of the big challenges with your Excel forecast is it gets out of state quite quickly. It's not necessarily because x there's any issues with Excel, but it's, you know, it's a manual product.

It's not got live data feeds, etcetera. Where where this sort of differentiates, you know, you're you're pulling the bank that you're pulling the data directly from the bank.

It's going directly into the ERP system. Your systems are live.

Data is going in there immediately, and it's working off your reconciled NetSuite data. So the forecast is always current.

Right? It's up to date. It's using live data in the system.

It's always tie it's tied to numbers you've already you already trust, and it covers all the transaction types. You know?

And that does include things like payroll, journal entries, etcetera. And and on top of that, the real key benefit, and we've seen a lot of time savings from this when we've done tests with clients, is the time that it takes to deal with a change or a variance within a forecast period can require a lot of effort.

Right? Going to find the right transactions, who do you need to pay, where do you need to get money from. When you have when you have solutions like the scenario studio, which as Anita showed you, you can query quite quickly with natural language, you're able to query that and get the answer of what you need to do within a matter of minutes.

So that's kind of why it sits as a really strong solution. Why why you'd wanna why you'd wanna use it over Excel.

It's a time save. It's already in NetSuite, and it's something that's using the data that you already have in the system.

Haley Ashe

That's fantastic. Thank you, Viraj.

Alright. So our our next question that was also presubmitted is what data trains the model and is our data used to improve forecasts for other customers?

Viraj Mistry

That's a fair question. So let me just sort of answer across both.

So firstly, the forecast built on your data. Right?

So we take your data within NetSuite, your reconciled transactions, your open AI, AP, and general activity. And the AI actually looks at your cash flow patterns.

So it's not a generic, you know, you know, assessment of cash flow patterns holistically. It's specifically to you.

And that kinda leads into the second, part of the question. The data is not used to train a shared model or to improve any other customer's forecast.

It's isolated to your environment. It's focused on you, and it's processed securely, and the intelligence is applied to your data only. So we're not pooling anything.

It's you're not feeding a communal model. It's a pretrained time series model which works specifically for you within your environment.

Haley Ashe

That's great. Thank you, Viraj. And then we did get one question from Claire.

So Claire asked, can it produce a thirteen week cash flow forecast by week for a number of subsidiaries on a consolidated basis? So, Anita, do you mind taking that one for us?

Anita Stamenkovska

Yes. Absolutely. Indeed.

Viraj Mistry

Alright.

Anita Stamenkovska

Within the setup assistant, we can select a custom date range, and we can include and exclude subsidiaries per needs. Now on the consolidated basis indeed, the cash forecast is built out of the currency of the headquarters subsidiary.

And then with the exception of the working capital insights, which at high level, they do represent the information within the consolidated currency. But when drilling down on subsidiary level, if the child subsidiary has a different subsidiary, then transactions are going to be defaulted in the subsidiaries currency.

Even further down, when drilling down on transaction level, we have capacity to display the transactions with their own, currency.

Haley Ashe

Perfect.

Anita Stamenkovska

Hope that. helps you.

Thank

Haley Ashe

Yeah.

Anita Stamenkovska

you.

Haley Ashe

That was great. That was great.

Thank you so much, Anita and Claire. I hope that answered your question.

So it does look like that was the questions that we had for this time. That said, if you have any others that come to mind, please feel free to go ahead and and send those through.

But we're gonna go ahead and get back into a couple of just other updates that we wanted to share with everybody while we have you today. So starting up first, for those of you who haven't met Zoe yet, and and by now, a lot of you probably have.

But for anybody that hasn't met Zoe yet, she's the layer that connects our agents to NetSuite our AI agents to NetSuite. So they're not guessing at your workflows.

They're working with your actual data, your subsidiaries, your billing schedules, your numbers. Two agents are live right now, free to start, the subscription AP intelligence agent inside zone capture.

You ask a question in plain English, get the answer straight from your NetSuite data. No more digging through reports yourself.

And a special announcement is that Cash Intelligence agent is inside zone reconcile is coming up soon, showing your cash position by bank account and subsidiary, catching unlocked or modified transactions before they become a fraud problem, and flagging the accounts that keep mismatching. So if you want to see Zoe in action, you can reach out to our team for a demo.

There's a there's a meet with us button up in the top right corner, or you can click the the link on the screen right now, if you'd love to see Zoe live, and we would love to love to show Zoe to you. Alright.

So the next update that we have is a little bit different. So it's more of an invitation, actually.

So we're launching a program here at zone, called build with zone. And the first place we're opening up is actually for zone liquidity, before it's even out the door.

So the idea here is simple. Instead of handing you a totally finished solidified product and asking you what you think, we want you in the room while we're still finalizing the build of everything.

You'd be working directly with Anita and Viraj and our product team and our engineering teams and helping us figure out what a connected treasury experience would look like in your business and what you'd like to see, where cash is headed, where how different decisions might change in the picture, etcetera. And we want it to be with your time, not just ours.

So you get direct access to the people actually building this, some paid recognition, which I'm sure nobody would would complain about, and a chance to connect with other finance leaders wrestling the same problems. So it's real, it's visible, and you get to say where where it goes.

So if this sounds interesting to you, there is a QR code on the screen, and there's also a ticker to sign up at the bottom of the the screen here to get started. We would love to have you participate in that with us.

Alright. And then just a couple other reminders, before we wrap up. We wanna make sure you know about the many, many resources available to support you throughout your journey with zone.

So whether you're looking to expand your product knowledge, troubleshoot an issue, or just stay up to date on what's new, we have got you covered in one way or another. So Zone University is your hub for on demand product training and education with content designed for both new users as well as experienced teams looking to just simply level up.

If you ever need assistance, we do have zone support, and we are always here for you through our support portal as well as our Zoe agent for technical support, which is available for twenty four seven instant answers. You can also explore the zone knowledge base for searchable articles, how to guides, and best practices tailored to real customer use cases.

And then finally, be sure to subscribe to the zone, our monthly customer newsletter. I dropped a link in the chat right now for you to sign up, but this is our our monthly newsletter featuring product updates, customer stories, upcoming events, and a whole lot more.

So we just really encourage you to take advantage of these resources. They're designed to just bring the most out of your zone resources and solutions for you.

So we would love to to have you participating in that. Alright.

And then just a couple events. We wanted to make sure we're on everybody's radar, that we will be at.

We'd love to see you at. So first up, September 17, we're hosting the NYC zone partner forum, which is a chance to connect with the NetSuite partners on finance innovation, AI, and procure to pay strategy.

Then on October 6, we're gonna be at SkillUp AI t2026 also in New York, talking about how AI is reshaping financial operations. And then in October, again, we'll be at the Digital Finance Show in London, The UK's flagship event for finance leaders covering embedded finance, open banking, and automations.

So details and registration inform information are here on the QR code on the slide, as well as on our zone and co website under our events section. So we would love to see you there.

So if you're interested, please sign up. Alright.

So last on my updates but not least, we just wanna make sure everybody's aware of our zone insider program. This is our customer advocacy community designed to give our zone customers or prospective customers like you a voice in shaping the future of finance technology, while also earning some recognition and rewards along the way.

So the program is intentionally flexible you get to choose how you want to participate, whether that's simply sharing a testimonial with us or completing one of our one of our surveys that we might run, leaving us a review on g two or just joining a customer spotlight story. You get to participate as much or as little as you like, and getting started is also super, super easy.

You can just visit our insider program site or scan this QR code on the slide to sign up in just a couple minutes. And from there, you can choose your activities that interest you and start earning rewards immediately.

So we would love to have you join that community. Now if you stayed with us this long, it is officially time to draw our winner for today's zone unplugged episode.

I'm gonna go on over our wheel of names here, and we're gonna see who today's lucky winner is. Alright.

That looks like Tonka is gonna be our winner for today. Alrighty.

Congratulations, Tonka. We will follow-up with you following today's call to share that gift with you. Alrighty, everybody.

That is a wrap on today's session. A big thank you to everyone who joined us today for this episode of Zone Unplugged.

We are so glad you came along for the ride. We really hope you're able to walk away today having learned something new about zone and about zone liquidity.

So like I said at the beginning, keep an eye on your inbox. You'll receive a follow-up email in the next few days with this recording in case you'd like to revisit anything we covered today.

And then be on the lookout. Our next episode is coming soon mid September, and it will be all about our latest AP intelligence agent within zone capture.

So if you'd like the sneak peek you saw today, you're gonna wanna make sure you check that one out too. So stay tuned for more details and registration info coming your way soon.

Thank you again everyone for joining us and have a great rest of your day. We'll see you next time.

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