And welcome to the Tray.io session here at the Enterprise Integration Summit, where we'll learn how modern integration technologies and best practices are helping companies overcome challenges and set the stage for next gen AI and automation projects.

We have two great speakers with us this morning, John Joseph, vice president of solutions, and Alexander Assur, sales engineer, both at Tray. Gentlemen, welcome.

Thank you. Glad to be here.

Thank you very much. Glad to be here as well.

Yeah. We're glad to have you guys with us. John, for his part, oversees both presales engineering and post sales professional services team at Tray for their integration solutions. He is a seasoned veteran in the enterprise integration space, having worked at MuleSoft and Airtable prior to joining Tray. And for his part, Alexander provides technical guidance and ongoing support to Tray's enterprise customers. He also has a decade of experience building and deploying automation infrastructures alongside iPaaS and RPA solutions.

And John and Alexander team up this morning for a great session. How to modernize your enterprise architecture through better integration. In twenty twenty four, as we all know, the push for AI enablement and intelligent workflows is a foot. But for many companies, it's increasing the strain on IT teams as well as inefficient company operations.

At the heart of these issues are often disconnected data, processes, and systems, so integration is a key element that can come to the rescue.

Today, John and Alexander will tell us how a novel integration architecture centered on data centric services can offer myriad solutions. And to make it all accessible to you, the attendees, you'll get a tech overview in this session, a demo, some best practices, and even a customer use case. So great session and great content here. Just a quick reminder before I turn it to John and Alexander, you could download today's slides.

Just click the big red button under the view area. You'll also see some other valuable resources, even access to a free trial. We highly recommend you take a look at that. They're all available with just one click.

And we love to get your interaction with the speakers. So to ask a question, just type in the submitted question box. So, John, Alexander, your turn. Tell us how to modernize your enterprise architecture through better integration.

Thanks, Vance.

I appreciate it. So this is a very interesting topic and one that's sort of near and dear to me is how should you be rethinking your enterprise integration strategy when you're embarking on iPaaS and automation.

So if we take a look at the challenges that many organizations are facing in today's digital landscape, it starts with the past decade of unprecedented growth in SaaS. And now we're starting to see a consolidation of SaaS applications in the space.

And this is forcing organizations to reshape how their businesses are operating.

There's also a noticeable disparity in how organizations approach automation and integration of those SaaS applications in their SaaS stack. Businesses face the challenge of connecting data, processes, experiences in a landscape where each solution has its unique approach, often leading to operational silos and inefficiencies.

We're also now entering a transformative phase in technology marked by the rising impact of AI. And it's not just reshaping individual applications, but it's fundamentally altering how enterprises approach decision making, customer experiences, and automated processes.

Addressing these emerging challenges requires more than just adopting new technologies. It demands a paradigm shift in our approach.

More importantly, it calls for robust and adaptable architecture that can serve as a foundational platform to tackle these challenges head on. And this approach involves rethinking how we integrate and leverage technology to create seamless, efficient, and future proof business processes.

And that architecture is the composability architecture. And at the core of this architecture lies what we call our data centric services.

This is a layer designed to handle diverse data types and sources, ensuring robust data collection, storage, and management. It's the bedrock that supports seamless data integration across multiple SaaS platforms, turning fragmented data into unified intelligence.

Building out our data centric foundation, the next layer is our process centric workflows. And this is where the magic of automation and efficiency takes place.

Here, Tray facilitates the design and execution of complex workflows, ensuring that every process from the simplest to the most intricate is streamlined and optimized.

This layer is crucial for eliminating operational bottlenecks and enhancing overall efficiency.

Capping our architecture is the experience centric automation layer. This tier focuses on end user or data ingestion experiences, harnessing the power of both data and process layers to deliver tailored and intuitive interactions.

From customer facing applications to internal tools, this layer ensures that every interaction is optimized to fit the form and function of the consuming application and or tool.

From an ownership perspective, the foundational tier of our architecture, the data centric services, is primarily owned under the stewardship of the integration specialist.

These experts ensure the integrity, availability, and security of data across various platforms.

Their role extends to some aspects of the process centric workflows where they oversee the seamless flow of data through automated processes.

Process centric workflows represent a collaborative domain. While business technologists ensure data flows effectively through these processes, business technologists also play a crucial role. They bridge the gap between technical capabilities and business needs, tailoring workflows to optimize operational efficiency and align with business objectives.

Experience centric automations are led by business technologists.

They harness the power of both data and process layers to craft personalized interactions, ensuring that every automated touchpoint resonates with end users and delivers tangible value.

Tray's approach is more than just these three layers. It's a well orchestrated system with clear ownership at each level.

Integration specialists and business technologists work in tandem ensuring that every aspect of our platform is expertly managed. This collaborative approach not only enhances the efficiency of operations, but also drives innovation and strategic alignment within business goals.

The modern digital workflow is the foundation or the building block of that composability architecture.

It's made up of logic and connectivity, implementing conditional logic and decision making capabilities within these workflows.

For example, different lead ingestion processes could be triggered based on lead source.

They also ensure seamless integration between different systems for a smooth data flow. They're API enabled. That means once a workflow has been constructed, being able to seamlessly publish that workflow as an API to make it consumable for others in the organization. It's composable, meaning taking design modular processes that can be easily modified or reconfigured.

For instance, adopting best of breed tooling to support lead processing should be simple.

It's scalable, ensuring that the lead processing workflow, as an example, can handle varying numbers of leads without performance degradation.

There's governance baked in, implementing policies and rules to ensure compliance with organizational standards and legal requirements such as data privacy laws.

Security as well, secure sensitive data through encryption, access controls, and regular security audits. And most importantly, AI enhanced, providing adaptive decision automation coupled with predictive analytics to deliver intelligent automation.

Now let's take a look at an example of this composability architecture using a lead life cycle use case.

Here, what we'd like to do is implement a common process centric workflow that operates consistently regardless of where we're getting leads from in terms of an intake process.

So what we can see at the foundational layer of this architecture are data centric services. We have all of the different applications in our rev ops stack that make up lead processing, And we've built a set of data centric services that sit on top of those SaaS applications to take care of validating, consent management, enriching the lead information, recording the leads in our CRM, performing attribution, scoring the leads, routing them internally, and then also doing engagement through our outreach tooling.

So we build these foundational services, and they're orchestrated through this lead process centric workflow that is agnostic of where the intake is coming from from a lead perspective.

And so now that we have this process centric workflow that manages our lead processing, we can make it available via form through the experience centric layer where marketing automation and revenue operations leaders can use that form intake to introduce additional leads. We can expose that via an API to third parties to intake of leads through an integration.

We can also take a file and take a CSV file with a set of leads that we pump through the lead processing workflow. So the point is we build this lead processing workflow once. We leverage these data centric services on top of our SaaS stack, and we can then expose that lead processing workflow to multiple sources of intake of leads.

So should the business decide that we want another form of lead intake, all we have to do is build another experience centric workflow to take in the lead from that additional lead source.

And, also, it's future proofed. So should we decide to retire ZoomInfo for enrichment, you can quickly change the Enrich data centric service to talk to another best of breed tool in the enrichment space, and our lead processing workflow remains whole. So it's an architecture that can help not only expand the business capabilities, but also future proof your investment in an ever changing landscape in SaaS.

Now the ways most organizations have tackled this composability architecture is by adopting different tooling at each one of these layers. So at the data centric layer, you may have a data integration tool that you're using, which comes with its own developer tooling and management infrastructure.

At that process centric layer, you may be leveraging an iPaaS or an automation tool or a combination of the tool of the two with its own development set of tooling and management infrastructure and hosting infrastructure.

And at that experience centric layer, again, that is probably another tool in the organization that handles user automations.

What's different with Tray is all three of those layers are delivered in one platform, and that addresses the operational silos and inefficiencies efficiency on delivering towards this composability architecture.

Now what is really interesting is once you continue to build using this composability architecture, a fabric of automation starts to emerge, and we call this the automation graph.

And what this essentially is is a well indexed dataset of user tasks, processes, and data integrations that make up your data flows in your organization, how they're surfaced up into business processes, and how end users or tooling is leveraging those business processes to represent how your business operates.

Well indexed dataset that we can now make available to large language learning models so that we can start to ask more interesting questions in terms of how, in the case of leads, how we're managing our leads, our efficiencies of these leads.

We can also introduce new business processes on top of this and take more of a governed approach to AI decisioning. Now from a product perspective, the Tray Universal Automation Cloud delivers on this architectural approach. It's a single product that provides the unification, automation, integration, connectivity, and embedding as opposed to a cobbled together set of products from other vendors. This allows for business technologists, integration specialists, and developers to collaborate on and deliver automations and integrations using intuitive experiences to fit the way each work, while also providing IT a common scalable platform to govern and secure.

This results in accelerated time to value, while also reducing support costs given that there aren't multiple tools to adopt or administer.

From an experience perspective, Tray Code, inspired by software developers who are looking to accelerate the development process to help make this experience possible, we've created an entire suite of APIs that leverage the Tray platform to create workflows, orchestrations, and connectors.

Tray build is a flexible low code automation and integration experience based on where Tray first started and launched is geared towards the business technologist and also integration specialist.

Tray chat, driven by generative AI, allows business technologists to engage with Tray by text chat. For example, you can ask questions such as send me in a usage report for the marketing department on our leads.

All three of these experiences are built on a single platform.

Tray Merlin, our generative AI service for developers, integration specialists, and business technologists is tailored to each experience.

There's also a set of common capabilities that take care of automation, such as the creation of workflows, reusable assets in terms of workflow snippets, or callable reusable workflows.

There's data integration providing support for complex data mapping, transformers of data fields using data mappers, along with support for data storage through searchable data tables.

From a connectivity perspective, there's access to over seven hundred connectors on the Tray platform.

So you can use one of these connectors or should a connector not be available to a system of record that you have in your SaaS stack, you can leverage our CDK to create your own. We also provide event based connectivity to eventing platforms as well.

And as I mentioned before, from an API management perspective, when data centric services, process centric workflows, or experience centric automations are created, those can quickly and easily be exposed as API, and policies can be enforced on the consumption of those APIs through our API management capability.

And then from an embedding or marketplace perspective, the ability to create prewired integrations that can be activated for marketplace solutions of your choice, as well as allowing you to bring your own workflow builder and round it out with the connectivity needs to SaaS platforms.

All of this is built on a flexible, scalable enterprise core, one core for all of these capabilities.

So looking at where Tray is today, we offer a more unified approach to iPaaS and automation.

Okay. So let's go ahead and take a tour of the product. And with that, we're gonna go ahead and hand it over to Alexander to run through a product demonstration of Tray.

Thank you very much, John. Let's dive in.

So on the screen here, I have a really simple example workflow built out in Tray. This is showcasing an ETL use case. So we're bringing in records from Salesforce using a webhook trigger, looping over them, performing a simple text transformation, and then pushing messages to Slack based on the data that we got back. Now when you're building a new workflow within Tray, you don't have all these connectors on the screen.

You just start from scratch choosing your trigger, and you can change that within the workflow builder as well. So I'm going to click these three dots to the left of the trigger here to show you the different trigger options that you have available for kicking off your workflow. You can run a workflow manually with the push of a button. This is really great for testing.

You can run workflows on a set schedule.

This also has advanced cron scheduling availability. So for instance, if you need a workflow to run every other Tuesday of the month, you can do that there. And then we've got external app event listeners. So for instance, a new opportunity in Salesforce like we have here or a message coming in from Slack, and all of these are just webhook triggers. So if you are looking for a specific event listener and you don't see it here, then we have a webhook trigger that you can use that will provide a URL that you can pass into the service.

All of these connectors came from our library here on the left, and we've got three different categories of connectors in Tray. The first and largest is our service connector library. These are the six hundred plus different services that you can integrate with out of the box in Tray. This is where you'll find Salesforce and Slack as well as Google, a full suite of Google connectors.

Microsoft connectors.

We've got AWS connectors. And if you ever don't find what you're looking for here in our library, then we still got you covered with our HTTP client connector. You'll sometimes see this referred to as the universal connector documentation as it allows you to integrate with any REST API, so pretty much every cloud based service.

For more technical users in the audience, all the standard REST API endpoints, methods here, plug in your URL, create a custom authentication, and you're up and running.

The next category of connector here is our helper connector library. These are primarily data transformation tools. So working with dates and times, parsing out lists, performing text transformations.

And then our core connector library deals with directing the logical flow of your automation, performing Boolean true false comparisons and, branching logic and working with loops, as well as FTP and SFTP client functionality and CSV editor functionality. So if you have any legacy systems that don't have API endpoints, then we can still get data into Tray.

And then if you ever need or want, you can embed Python and Java script directly in a workflow for more granular control over the flow of data.

I'm gonna step through the workflow a little bit here just to show you what it's like to build in Tray.

So when you bring a new connector onto the canvas, for instance, this loop, it'll be unconfigured like this, and you'll be able to set your mappings. So for instance, this list data type for our loop, I can map in a few different ways. The easiest is to select this circle to the left of list and drag what we call our connector snake over to the connector we're getting output from. So we're mapping the output of one to the input of another.

It'll highlight the data type that we can work with. This list data type has square brackets, erase everything else out. I can click this, and now it's mapped. And so every time that this workflow runs, it'll loop over this list, and we can apply logic to each iteration of that list.

You'll see here that while I have this connector selected, that you can see all of the inputs and outputs that's fed into it and from it. So down arrows for all the data feeding in and horizontal arrows showing where it's going.

Our text helpers connector has a lot of powerful functionality here. So you can perform base sixty four encoding. You can concatenate data. You can check to see if strings match particular patterns, etcetera. A lot of different data transformation tools as well as searching within text that you brought into Tray.

Finally, sending a message to Slack.

Now, of course, with any connector, any service connector you authenticate in, choose your auth here. If it's an OAuth based service, it's nice and easy. You just sign in like you would into Slack itself in this case.

Select the operation that you want from this drop down. In this case, I chose a send message operation, and then configure your inputs. Now part of what we do here is to try to abstract away the difficult stuff. So this send message operation isn't just one API endpoint.

We also bring in a channel retrieval endpoint to populate a dynamic drop down list. I can select my own channel and then pass the messages into it. And here, I just basically select whatever I want from the previous steps in the workflow. I could select a value from this loop or maybe the output of this concatenation step, bring it into that field.

And then when the workflow runs, it uses that output.

While you're building in Tray, you'll have access to Merlin, our AI copilot, who can answer questions about different pieces of functionality like the cron expressions I mentioned earlier. Merlin can help you write a cron expression so you don't need to look that up on the fly, as well as building with in Tray. You can ask Merlin to create workflow steps for you on the canvas.

One more thing I'll touch on here while we're talking about automation and integration at scale.

Our API management tool suite allows you to surface entire workflows as API endpoints. So if I switch this over to a webhook trigger or if I use a preconfigured webhook triggered workflow, you can create an operation for an API endpoint.

Give it a name, a description, the API method that you wanna use, and then you get to choose your endpoint path.

And then that's it for configuring an operation. But really powerful underlying all of this is our access control management. So you can designate roles, different roles of different levels of access to your API, as well as policies, including throttling.

So you can add throttle buckets to control how frequently a given endpoint is hit within Tray and have full granular control over your data throughput.

Another thing that I'd like to show here is just the power of our UX. So when you're building any workflow in any tool, sometimes they can get complex. I'm just gonna copy this loop here to give you an example of what that complexity could look like. You can copy and paste different components within Tray, nest loops together, and bring in Boolean conditions.

And as you build, you'll notice here, you're able to zoom out. You can see the workflow at scale, see exactly what's happening, identify all of your loops, all of your Boolean conditions, and get a really good high level view for how your workflow is functioning.

Alright. Thank you all. I hope you enjoyed the demo, and I will kick it back to John.

Thank you, Alexander. So next up, we're gonna take a look at a customer case study that is in alignment with what you saw in the product today. And we're gonna be highlighting some of the benefits that the customer realized based on leveraging Tray for their integration and automation strategy.

Here we have a digital marketing agency that scaled their business by infusing AI with Tray. Based this customer's resources that were available, they were struggling to scale without significant cost increases, such as handling things like lead management, client reporting, and customer demand operations.

By infusing AI into their processes, they save over six hundred thousand in spend, increasing campaigns managed per FTE, reducing manual reporting time, and increasing margin per opportunity.

And the biggest call out here is just how quickly they could operationalize AI compared to any other company they were competing with.

Again, this can be a competitive advantage to many organizations.

And this is just one of many examples where Tray has helped transform enterprise velocity across a number of our customers from the smallest of start ups to the largest of enterprises.

So to wrap, one of the things that you wanna keep in mind when you're embarking on your iPaaS and automation journey is think about deploying a unified approach across those technologies.

By deploying a unified approach, that's going to get you closer towards this autonomous enterprise.

By having a single approach and adopting technologies and a platform that aligns to that approach makes it easier for you to not only rationalize your investment in SaaS applications, but also position you better so that you can take advantage of generative AI, the benefits that it brings in a governed fashion so that you can embark on your next journey around AI.

So that concludes the formal presentation. Let's go ahead and hand it over to Vance for some questions from the audience.

John. Alexander.

Wow. What a great session. And this whole idea of using the platform of Tray fueled by AI to join this world of traditional iPaaS or integration with automation and autonomous systems, really, really awesome. Really great vision and implementation. Fun session. Thank you.

Yeah. You bet. Thank you. Thanks for having us.

Thank you very much, Vance.

You know, luckily, for all of us, you left us some time for questions. So with your permission, I'm gonna just jump right into them. One of them and this kind of tackles the main theme of your whole presentation, guys. You know, it's so fascinating. I think many of the attendees here have looked at integration and automation as two separate disciplines, even two separate teams to work on, and you're really leaving the impression that those are either intertwined or may actually become the same thing. Talk about how Tray is enabling companies to kind of embrace both and have more of a heterogeneous approach toward how they think about integration slash autonomous systems?

Yeah. I appreciate it. That's a great question, Vance. So if you take a look at what both automation and iPaaS are solving for, it's to connect data processes and experiences so that you can deliver capabilities back to the business.

But the way that most organizations have adopted it traditionally is they've leveraged different platforms to drive their iPaaS initiatives and also to drive their automation initiatives, and that creates operational inefficiencies because you have siloed systems.

And so by going with a more unified approach, it drives more consistency and standardization around how you should be thinking about which processes fall into sort of the automation realm and which processes fall into that traditional iPaaS integration realm and how the two can be leveraged together to deliver business capabilities.

Now how Tray does this uniquely is that we have one platform that allows you to build very complex integrations either through a builder experience or through code and what we call Tray build and Tray code.

We also provide that same platform that allows maybe business technologists, folks that may not have that deep integration experience, but understand their business processes, and they understand tasks that need to be automated. And so we provide that builder experience, which we saw on the demo that we call Tray build on our platform. We also provide a generative AI approach to creating automations on our platform that we call Tray chat. So depending on your skill set, we kinda meet you where you're at with our platform.

You know, it's really awesome. Really great. In fact, the demo really did suggest a lot of secret sauce going on, John, to make a lot of that capability and extensibility for both high-tech and low code or citizen folks to work on things. Alexander, anything you could share about the secret sauce that's going on underneath that makes Trace so fungible for whatever my use case or my staff might be?

Yeah. Absolutely. So our product is kind of built in a few different layers. The first and foremost UI experience is our platform builder, the way that you can drag and drop different connectors together to integrate any service and perform different automated tasks in between them. But all of this, of course, is built on top of API endpoints that we surface to our customers. So if you have a development team that would prefer to write code, deploy custom connectors, or even invoke our connectors through their own in-house built services, we make that available to you. So it's really kind of a top down fully complete product that allows both highly technical teams to work quickly and efficiently as well as less technical builders to construct workflows within our UX.

Excellent. Excellent. You know, Alexander, you mentioned APIs. It's certainly something that our audience is well familiar with probably for more than a decade. We've got hands-on professionals that have been working with APIs. The question here says, what API functionality is available for interfacing with Tray itself such as importing or exporting workflows or working with other legacy via the process or systems?

So like I mentioned, we surface our connector library through an API end point. You can also, of course, surface the list of connectors, all of the operations within those connectors. But on top of that, we make available the ability to import and export individual workflows like you mentioned, as well as full project folders. And then if you are productizing your integrations for end users, we have a suite of endpoints for surfacing groups of workflows as complete productized solutions, as well as creating and updating authentications and managing the end user objects within Tray itself.

To put all that together with the general theme of the session, your platform really has provided a way for me to easily modernize my legacy environment. For instance, there are some capabilities in Tray that I might not have in my integration infrastructure that if I use the APIs correctly, I'll all of a sudden kind of inherit those powers. Is that a fair way to think about it, Alexander?

One hundred percent. So we standardize the schema using our connector and point as an example here that allows you to build out the infrastructure and scale up. So you just have to learn the schema one time, build your own product out to reference it one time, and then scale up by invoking it for new connectors as you deploy new features to your product.

That is really awesome. Really. It's almost like Lego blocks for my legacy system. Really fantastic.

But another thing, beyond the design time of integration, a lot of our attendees worry about the operational and integrity of the integration. And the question here says, how does Tray handle error handling?

That's a great question. We have a few different features that allow you to handle it at different levels within the platform. So, of course, at the individual connector levels, so kind of low down within the UX and you're building workflows, you can have connectors stop a workflow completely if they encounter an error. You can continue workflows.

Maybe a connector isn't critical to the workflow's functionality. And then you can also split the path. So you can have manual air handling. Let's say your Salesforce connector has encountered some kind of unhandled air.

You can have a path to catch that, maybe send an email to your CS team, and then have all of your happy path functionality on the other side. And then at the top, so kind of the high level scope, you can have alerting workflows that listen for errors in any number of grouped workflows that maybe aren't handled at the connector level. So we have kind of a whole robust suite of ways to monitor for these errors and handle them depending upon your needs.

Yeah. I was just gonna say it does sound like a nice combination of both monitoring and management, but also the ability to kinda pop the hood where I need to and get in there and actually intervene and make those reparations or adjustments as needed.

A hundred percent. And, you know, what kinda goes along with this too is our execution logs are live. So as your workflows run, let's say you have a workflow that takes ten minutes to execute, you can go in, see where it's at, what it's doing. And we also provide the ability to stream those logs live to your own data analytics center like a Datadog or a New Relic.

Oh, wonderful. Wonderful. You know, I think time is just about up, but this has been such a great session. Time's rushing by really fast. You know, one other theme that comes up a lot, especially when we talk about AI and Gen AI, is the idea of responsible AI. And the question here says, what ethical consideration should we take into account when implementing AI driven decision making, especially when using an autonomous enterprise?

This is a very loaded question, and I'm gonna try and respond to it somewhat succinctly as best as I can. There's probably eight things to consider when you're looking at the ethical implications of automation and Gen AI, the combination of the two. And bias and fairness is one. So, you know, AI systems can inherit biases from their training data, and they can, as a result, create unfair decisions and discriminate certain types of groups. So what enterprises must do is, strive to identify and mitigate biases in their AI algorithms to ensure that there's fairness in the decision making.

There needs to be transparency in how AI systems make decisions, especially when those decisions impact individuals directly.

The third is accountability. We need to determine who in the organization is responsible for the decisions made by AI.

Autonomous enterprises must establish clear accountability around AI decisioning.

Privacy is the fourth. AI systems often process vast amounts of personal data, and enterprises must ensure that that data is collected, used, stored in ways that protect the individual's privacy and comply with data protection laws.

The fifth is around security. So as AI systems become more integral to your operations, there's the potential impact of security breaches. So enterprises, again, have to safeguard their AI against potential malicious attacks.

The sixth is the social impact.

And this is sort of the broader impact of AI on society, including job displacement and social inequality. Those should be considered when you're going down this path of the autonomous enterprise. Enterprises should actively work towards mitigating any negative social impacts such as by supporting workforce transition to new roles as an example.

Then there's sustainability. There's the environmental impact of training and running large AI models. It can be pretty significant in terms of the power consumption that it can have in the environment.

And so enterprises should consider the sustainability of their AI practices and look for ways to minimize carbon footprint and power consumption. And then the last is on consent and autonomy. When AI systems make decisions that affect individuals, it's crucial to consider the autonomy of those individuals.

And so enterprises should ensure that individuals have consented to the use of their data in a way that is informed and voluntary. So those are my recommendations on how organizations should be thinking about the ethical implications of adopting an autonomous enterprise in AI.

Wow. Those are amazing recommendations and very well thought out. Obviously, Tray has done a great job in articulating a whole dimension of AI considerations across the landscape, not just among the staff, but among customers and just the company in general.

Really great response, John.

Thank you. Thank you.

With that, I see time has expired, but we have one last piece of business before you go. I know that our attendees wanna learn more about how to bring together the worlds of traditional iPaaS or integration and autonomous systems. I wonder if you could suggest a way that people could learn more about Tray, maybe go hands on or take a one on one demo. What are some options we have?

So check us out at Tray.io. You can always go and sign up for a demo of the product. You can also sign up for a trial of Tray as well. Be happy to sit down and have a one on one conversation with from one of the folks around our account teams having a one on one conversation about your automation use cases and iPaaS use cases and how we can help deliver on those using more of an AI infused approach.

Excellent. Next steps. Excellent. John Alexander, thank you very much for a great session. And another note to our attendees, really excellent questions.

We really appreciate it. Thanks very much for coming, and thanks very much for the great, hands on demo. Fantastic.

Thank you, Vance.

Thank you very much, Vance.

Our pleasure. Our pleasure. And a quick note before we wrap, John and Alexander both mentioned some really great resources. We've got many of them here right in the breakout room.

Feel free to take a look and click away, all available with one click and not a lot of registration required. And as you can tell, the innovation is just beginning at Tray.io. Here's the slide. It'll take you directly into the Tray website.

Download the terrific slide deck this morning, and all these links will be live. You can get directly to the Tray website to learn more. Thanks again to our speakers and audience. Thanks again for some really great and lively questions.