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Hey there and welcome to Swim with Sharks, a deep dive into generative AI for the enterprise.
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I'm your host, Kevin Dean, CEO of Mantle Byte.
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And I am thrilled to have you join us on this exciting and dynamic world of artificial intelligence for the enterprise.
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So we're excited to have with us today, Steven.
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Steven is from trey .ai, name change there.
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Steven, welcome to the show.
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Why don't you tell us a little bit about yourself?
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Thank you.
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Thank you.
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Yeah.
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The rebrand was, was, was fun to go through.
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but yeah, my name is Steven Stofer.
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I'm the director of automation solutions here at trade .ai previously trade .io.
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We'll get into that a little bit here, in a second, but, yeah, my, my roots, is marketing automation.
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And then that, that was like about a decade ago.
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My, kind of entered into the space under marketing automation, email marketing that kind of blossomed into, revenue operations.
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And then over the last several years, man, I've just been diving into the AI space.
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lots of fun things.
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I've my background is on the enterprise side, I've worked for a company called Dex Media.
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If you remember the Dex phone books, and then they merged or acquired Yellow Pages.
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So I've been very much on the enterprise side.
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And then I've also worked at agencies and startups and nonprofits.
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So I've kind of seeing the gambit when it comes to organizations.
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Well, that's so cool.
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It's nice that you have such a broad experience.
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So tell me, right now, there's so much conversation about generative AI.
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What's one surprising thing that you've seen that's going on and something that you think people should know about?
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Yeah.
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I'd say it's like, it's for everybody.
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Like, I think there's some voices out there in the market who talk about all of the crazy, awesome stuff that you can do with, with artificial intelligence from, you know, embedding into your platforms and machine learning and, all, and it can kind of scare people, right?
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It can be like, that's, that's not for me.
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I'm not a technical person.
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I don't know how to work with servers or APIs or anything like that.
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But, you can like any level of, of, where you are from your journey with an AI from a total newbie who doesn't know how to code, you can get a free open AI chat GPT account and start using the generative AI piece for generating content and ideation and learning about ways that you can incorporate it into your role, all the way up to the engineering type folks.
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So if you are a super technical person, you can build some really, really cool stuff for your organization.
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So I guess I would say don't be scared of it.
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and dive right in, it's the future of organizations, it's the future of the way that we're gonna do work.
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So don't be scared, it's for everybody, not just the engineering.
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You know, I absolutely love that.
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And that's a good way to, you know, kind of frame our conversation.
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AI is for everybody now.
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Like it's kind of leveled the playing field to some degree.
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But then would you think that while it's leveling the playing field, there's still this thing where enterprises can take it to the next level to kind of, you know, say, now I'm going to separate, use it to separate myself from the competition.
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Yeah, I mean, absolutely.
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mean, even Trey here, we saw that as an opportunity to separate ourselves from some of our competitors.
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So previously we were Trey .io.
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Trey is an IPass, an integration platform, so you can plug in all of the amazing tools that you have in your organization and orchestrate that and get things connected in a really good and complex and custom way.
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But we also saw an opportunity where like, look, AI is coming.
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of the marketplace, that is an additional tool that folks are trying to make heads or tail of and kind of get behind and integrate into your systems.
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So not only are we building our own native tray AI tools where you don't have to get another subscription, you don't have to get authorization from another company and go through compliance.
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It's just baked right there into the platform.
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But then you can also plug in, you know, something like OpenAI or Bedrock from AWS.
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So yeah, it's definitely an opportunity for companies to set themselves apart.
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And if you don't, you're going to be a step behind the curve.
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I like what you're saying, right?
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And so it gives businesses the opportunity to set themselves apart, you know, to get ahead of the game.
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I think that there's this interesting concept.
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To and I'd love to your take on this, you know, you mentioned that now gen AI is for everybody I was literally talking to a friend who used it to help their kid, you know schools back in session They use it to help their kid solve some math problems, right something that they didn't understand easy to help with that So that's one kind of area of it But when it comes to the enterprise, okay now we're talking about something completely different now.
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We're in the enterprise space Sometimes I think that organizations seem to think that, it's just as easy as helping my kid with their math problem on school.
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Is that correct?
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Is it really that easy when you're looking at implementing GNI in the enterprise?
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Gosh, that's such a loaded question.
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I'm gonna go with it depends.
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So it depends in that if you're trying to empower your teams to do simple tasks like math problems or maybe coding would be a good example where you want to enable your developers to develop JavaScript, HTML, CSS, Python, whatever languages that you are.
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Sure, they can go in, they can do prompt if they know exactly what they want.
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it in.
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But where it gets difficult, and this is the maybe piece of it is, is AI is only as good as your data and it's only as good as the context it has.
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So do you want your marketing team to be saying, know, chat GPT, create me an email for this product, but it can't create the email unless it knows what is your corporate tone of voice?
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What is your products and services?
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Who are you targeting?
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What's your target audience?
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emails have performed in the past really, really well, and can we use that information to dictate the email that's being created?
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yeah, mean, out of the box, sure, you can have a very rough version of generative content being created, but if you really want to unleash the power of it, you have to feed it data, you have to feed it context.
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And that's where kind of the magic happens with AI in deploying it to maybe non -technical folks who don't know that they need to feed it this context.
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So that's on the enterprise deployment team to be setting those guardrails around security and feeding it the context that's appropriate for the teams that are leveraging it.
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That's very insightful, right?
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So what I'm hearing is that, you know, while it can make your life easier, there's some work that has to go into it in order to get it to the place where now you can start, you know, leveraging it across the enterprise.
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I think of it more of like a scale of usefulness.
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if you don't do any pre -work and you don't do anything, yes, you can use it, but it's not going to be as helpful if you do the pre -work and you fix the underlining data, if there's data issues and you can feed it the context of what is your corporate tone of voice and, how do you word things and how do you position your products and services and, know, what would a, an upsell look like in your customer buying journey?
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so.
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So it just becomes more useful the more time and effort you put into it.
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But that's the great thing about AI is it is useful out of the gate.
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It just might not be as useful if you were to have a little bit more of a thoughtful approach to deploying it within your organization.
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Yeah, I love that.
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You really need to have a thoughtful approach to how you're going to deploy it in your organization in order for you to get the biggest bang for your buck.
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That makes a lot of sense and that's pretty smart.
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What other challenges do you see when you're talking to organizations about taking the next steps with GEN .AI and how they can use it to automate their business processes?
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What other challenges, you mentioned data is a challenge, but what other challenges do you see organizations running into?
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our routine link.
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Yeah, I mean, I think...
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The main challenge is it can do so much.
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It, can be difficult to whittle that down to what the most impactful thing it can do for your organization.
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You're to have a lot of different teams that do a lot of different things.
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they operate differently.
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So figuring out where you start is as oftentimes a challenge in that it can be actually so difficult that people just don't even start.
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Like they don't even do anything because they're like, gosh, this is going to be way more difficult than what we can handle.
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So.
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I guess my advice there is like start simple, start with a beta test group, maybe start with a specific team that maybe is conducive to change more than other departments, right?
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So like your legal department, your HR department, your maybe corporate IT department, they might be a little bit slower to adapt than maybe your marketing team, right?
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Your marketing team around content generation, they might be more quick to adapt to testing it out.
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But also security, right?
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So you do need a loop in your IT team.
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You need to go through the checks and balances.
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If you're SOC 2 compliant organization, you need to make sure that you're adhering to all of the controls that you've agreed upon for your audits.
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But yeah, would say like starting small because that can be very intimidating.
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And then also we talked a little bit about data, right?
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So data and fixing the data is it can be a big blocker to launching something like AI in an organization.
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it's just not clean.
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Yeah, that makes a lot of sense.
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That's very practical.
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Now, for our audience, one of the things that we want to do is help them understand the tools and the platforms that are out there.
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Can you explain to our audience what exactly does Tray .ai do?
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Yeah, so Trey is an integration platform.
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It's an Ipass solution, so integration platform.
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And then it connects your tools.
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So just to give you a very simple use case, let's say you have Salesforce or HubSpot or whatever your CRM choice is.
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And let's say if you have a HubSpot, a deal goes closed one, or if you have Salesforce, an opportunity goes closed one, and you want to do something with that information.
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Let's say you want to, one, give a little shout out to your team.
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You want to send like a Slack notification or a Teams notification to be like, hey, so and so closed the deal.
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It was like this.
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This is a really good opportunity for the team.
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Get some corporate communications out.
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But then also maybe what you want to do is you also want to take the opportunity and impact the customer experience, right?
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So you want to send out an invoice.
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So you need to create invoices.
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You need to share that information with QuickBooks or NetSpeed or whatever your ERP is.
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And then you need to communicate.
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financing you need to make sure that there's follow -up.
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Make sure they pay their bill on time, right?
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So, Trey is kind of the platform behind the scenes that orchestrates the communication between all of your tools.
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And that's just one use case on maybe a quote to cash type, use case, but there's like tons out there from marketing, having an ingestion for leads, right?
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So you might use, Google or Facebook or LinkedIn lead gen forms for lead capture.
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but getting that connected into your tech stack easily is really difficult.
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Normally you have to go into LinkedIn, you have to log in, you have to export a CSV, you have to import that CSV into your CRM.
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It can be very time consuming.
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Trey basically handles the automation from start to finish.
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So you can plug LinkedIn into Trey, plug your CRM into Trey, and when someone fills out a form, it's milliseconds of that record getting into wherever it needs to go.
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Well, that's pretty cool.
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Now, you did mention early on that Trey's gone through a rebrand focusing on AI.
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Can you tell us about some of the features that Trey now has from a generative AI perspective and how that might work within one of these workflows or for an organization?
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Sure, yeah, well, So we've released a series of connectors called Merlin connectors.
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So for an example, we have Merlin guardian, which tokenizes your data.
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So if you're looking to do something with, let's say, open AI, let's say you want to, write an email for a specific person, you know, you have their name, you have their title, you have their, address information and you deploy something to marketing for them to use.
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They might actually put all.
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that information into their prompt and say, Hey, write an email for, and then they put in sensitive information, PII sensitive information.
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Merlin guardian, essentially, if you were to put the build this on the trade platform, you can push it through Merlin guardian.
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It'll actually tokenize that information.
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if it sees, marketing team put in a name, they put in a last name, they put in an email address.
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We do not want to send that to open AI.
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So Merlin will essentially tokenize it, send the tokenized version of their prompts to open AI.
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open AI will have the context of what those tokens are, right?
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It'll know it's a name, but not know what name it is.
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It can write that email, come back with the response and tray.
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And then Merlin guardian has another feature where it de tokenizes it.
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So it converts it back to that personalization.
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And then it sends that de tokenized version back to your marketing team.
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So they don't actually know that their sensitive information, they fed into the platform, never actually made it to open AI.
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but the experience is seamless, right?
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It doesn't throw an error message.
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It doesn't say, don't give sensitive information.
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It is all handled on the backend securely, open AI, never got access to it, but you can deploy it in a way where you've set those security guardrails for marketing.
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That is an amazing piece of functionality, especially now in this world where we have to be very cognizant about security and privacy and data, what we're sharing and how we're sharing it.
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That's an awesome feature and functionality that can be built into your workflows by using Trey, right?
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So when you think about...
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just when you look out into the space in general and you see what generative AI can do and there's so many things that it can do.
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What are some features or capabilities that you believe are missing in some of the tools in the enterprise space?
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Yeah, I would say security might be a concern.
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I think that folks are moving very fast.
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And that's the reason why we've been built the example I told you to begin with is because folks are moving so quick.
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It's difficult to trust that some of these smaller AI shops who might do the thing you need it to really, really well.
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But they're very small team, right?
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They may not be sock to compliant.
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They might not be HIPAA compliant.
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They might not be GDPR compliant.
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So you know, making sure that you've built those controls on your end versus depending them to is big.
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And then the other big one that I would say is, you know, early on in this conversation, we talked about like a knowledge base or context around prompt building.
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You know, unless you've built your own chatbot on OpenAI, there is an opportunity to upload knowledge base articles, but they don't make it very easy to add that additional context.
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So I think that that could be bit of a gap right now, but everyone is scrambling to fix that so Yeah, I'd say that like security and then also the context piece of prompt generation Yeah, those are two great things that need to be addressed, but I think exactly like you said, like those are things that people are scrambling to fix as quickly as they can.
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So what about the future?
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What do you think some cool things are gonna come in the near future?
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What excites you about, you know, what's next?
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Yeah.
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It's interesting question.
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It's hard to predict what the future is going to be, but we are definitely at the, exponential growth AI journey, right?
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I mean, it, when did open AI release chat GPT like late 2022.
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And here we are only a couple of years later and almost every company, every conference is talking about AI.
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just within a couple of years, it's completely reshaped the landscape.
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So another two years, I don't know what that's going to look but I know that like you mentioned earlier, it's level setting the playing field.
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So the way that we do work is going to be different.
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So if you're a developer, your, your day -to -day workload is going to look different.
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It's going to be the co -pilot of every employee in every organization.
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And you know, here in Dallas, we actually have McDonald's that's fully autonomous.
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There is no person who hands you your food.
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There actually is no person you can even talk to when you go into the McDonald's.
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So if you want to look at a weird AI future, take a look at the McDonald's in Fort Worth because it's completely autonomous.
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Now that's an extreme example.
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I don't think every restaurant in America or the world is going to be handled by robots in the near future.
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It's definitely going to be ingrained and there's going to be basically HR onboarding courses for, you know, there's gonna be like risk mitigation and there's gonna be like your AI certifications.
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So it's, going to get interesting real quick.
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That is very cool to think about going to McDonald's and not seeing a person at all.
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And honestly, some of the McDonald's I've been through, I'm excited.
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Like, can you please get back to McDonald's next to my house?
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You're like, I want my burger and french fries as quickly as possible.
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I don't care if it's a robot that gives it to me.
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Yeah.
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Yeah.
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man, this has been a fun conversation.
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Hey, you know what?
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Can you tell us a little bit about what you do when you're not geeking out over Gen .ai?
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What type of fun things you like to do?
00:20:02.349 --> 00:20:02.611
man.
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All right.
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So I'm I love rockhounding and I don't know if you know what that is, but basically it's what you say.
00:20:10.099 --> 00:20:10.912
No, me about it.
00:20:10.912 --> 00:20:11.313
want to know.
00:20:11.313 --> 00:20:12.028
I don't know.
00:20:12.028 --> 00:20:27.683
So rock counting essentially is you basically looking for like special like certain minerals like gold, silver, pyrite, like quartz, like, you know, you kind of like for like fun rocks and gemstones kind of out in nature.
00:20:27.683 --> 00:20:29.095
So that's like that's really fun.
00:20:29.095 --> 00:20:30.775
I'm like big into nature.
00:20:51.690 --> 00:20:53.992
that is very, very cool.
00:23:58.593 --> 00:24:04.056
Steven, thanks for joining us on the show today and thanks everybody for tuning in to Swimming with Sharks.
00:24:04.056 --> 00:24:12.299
And please check us out past, post, future episodes at manobyte.com forward slash SWS for Swimming with Sharks.
00:24:12.299 --> 00:24:14.770
Thanks everybody and we'll see you the next time.
00:00:02.241 --> 00:00:08.112
Hey there and welcome to Swim with Sharks, a deep dive into generative AI for the enterprise.
00:00:08.112 --> 00:00:11.345
I'm your host, Kevin Dean, CEO of Mantle Byte.
00:00:11.345 --> 00:00:20.568
And I am thrilled to have you join us on this exciting and dynamic world of artificial intelligence for the enterprise.
00:00:20.568 --> 00:00:23.940
So we're excited to have with us today, Steven.
00:00:23.940 --> 00:00:28.161
Steven is from trey .ai, name change there.
00:00:28.161 --> 00:00:29.582
Steven, welcome to the show.
00:00:29.582 --> 00:00:31.713
Why don't you tell us a little bit about yourself?
00:00:31.803 --> 00:00:32.293
Thank you.
00:00:32.293 --> 00:00:32.664
Thank you.
00:00:32.664 --> 00:00:32.954
Yeah.
00:00:32.954 --> 00:00:35.915
The rebrand was, was, was fun to go through.
00:00:35.985 --> 00:00:38.277
but yeah, my name is Steven Stofer.
00:00:38.277 --> 00:00:43.639
I'm the director of automation solutions here at trade .ai previously trade .io.
00:00:43.639 --> 00:00:52.192
We'll get into that a little bit here, in a second, but, yeah, my, my roots, is marketing automation.
00:00:52.192 --> 00:00:54.594
And then that, that was like about a decade ago.
00:00:54.594 --> 00:01:03.942
My, kind of entered into the space under marketing automation, email marketing that kind of blossomed into, revenue operations.
00:01:03.942 --> 00:01:10.055
And then over the last several years, man, I've just been diving into the AI space.
00:01:10.055 --> 00:01:11.316
lots of fun things.
00:01:11.316 --> 00:01:16.350
I've my background is on the enterprise side, I've worked for a company called Dex Media.
00:01:16.350 --> 00:01:23.367
If you remember the Dex phone books, and then they merged or acquired Yellow Pages.
00:01:23.367 --> 00:01:26.009
So I've been very much on the enterprise side.
00:01:26.009 --> 00:01:30.531
And then I've also worked at agencies and startups and nonprofits.
00:01:30.531 --> 00:01:34.227
So I've kind of seeing the gambit when it comes to organizations.
00:01:34.564 --> 00:01:35.766
Well, that's so cool.
00:01:35.766 --> 00:01:38.278
It's nice that you have such a broad experience.
00:01:38.278 --> 00:01:45.073
So tell me, right now, there's so much conversation about generative AI.
00:01:45.114 --> 00:01:53.001
What's one surprising thing that you've seen that's going on and something that you think people should know about?
00:01:53.391 --> 00:01:54.376
Yeah.
00:01:55.513 --> 00:01:57.685
I'd say it's like, it's for everybody.
00:01:57.685 --> 00:02:12.882
Like, I think there's some voices out there in the market who talk about all of the crazy, awesome stuff that you can do with, with artificial intelligence from, you know, embedding into your platforms and machine learning and, all, and it can kind of scare people, right?
00:02:12.882 --> 00:02:14.771
It can be like, that's, that's not for me.
00:02:14.771 --> 00:02:15.913
I'm not a technical person.
00:02:15.913 --> 00:02:19.895
I don't know how to work with servers or APIs or anything like that.
00:02:19.895 --> 00:02:45.175
But, you can like any level of, of, where you are from your journey with an AI from a total newbie who doesn't know how to code, you can get a free open AI chat GPT account and start using the generative AI piece for generating content and ideation and learning about ways that you can incorporate it into your role, all the way up to the engineering type folks.
00:02:45.175 --> 00:02:50.109
So if you are a super technical person, you can build some really, really cool stuff for your organization.
00:02:50.109 --> 00:02:54.771
So I guess I would say don't be scared of it.
00:02:55.407 --> 00:03:00.556
and dive right in, it's the future of organizations, it's the future of the way that we're gonna do work.
00:03:00.556 --> 00:03:04.905
So don't be scared, it's for everybody, not just the engineering.
00:03:05.088 --> 00:03:07.289
You know, I absolutely love that.
00:03:07.289 --> 00:03:11.950
And that's a good way to, you know, kind of frame our conversation.
00:03:12.251 --> 00:03:13.860
AI is for everybody now.
00:03:13.860 --> 00:03:18.612
Like it's kind of leveled the playing field to some degree.
00:03:18.612 --> 00:03:33.967
But then would you think that while it's leveling the playing field, there's still this thing where enterprises can take it to the next level to kind of, you know, say, now I'm going to separate, use it to separate myself from the competition.
00:03:33.967 --> 00:03:35.359
Yeah, I mean, absolutely.
00:03:35.359 --> 00:03:43.192
mean, even Trey here, we saw that as an opportunity to separate ourselves from some of our competitors.
00:03:43.192 --> 00:03:45.492
So previously we were Trey .io.
00:03:45.492 --> 00:03:59.818
Trey is an IPass, an integration platform, so you can plug in all of the amazing tools that you have in your organization and orchestrate that and get things connected in a really good and complex and custom way.
00:04:00.118 --> 00:04:03.984
But we also saw an opportunity where like, look, AI is coming.
00:04:03.984 --> 00:04:13.741
of the marketplace, that is an additional tool that folks are trying to make heads or tail of and kind of get behind and integrate into your systems.
00:04:13.741 --> 00:04:26.050
So not only are we building our own native tray AI tools where you don't have to get another subscription, you don't have to get authorization from another company and go through compliance.
00:04:26.050 --> 00:04:28.132
It's just baked right there into the platform.
00:04:28.132 --> 00:04:34.247
But then you can also plug in, you know, something like OpenAI or Bedrock from AWS.
00:04:34.451 --> 00:04:39.697
So yeah, it's definitely an opportunity for companies to set themselves apart.
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And if you don't, you're going to be a step behind the curve.
00:04:46.588 --> 00:04:48.060
I like what you're saying, right?
00:04:48.060 --> 00:04:53.829
And so it gives businesses the opportunity to set themselves apart, you know, to get ahead of the game.
00:04:53.829 --> 00:04:58.184
I think that there's this interesting concept.
00:04:58.749 --> 00:05:25.081
To and I'd love to your take on this, you know, you mentioned that now gen AI is for everybody I was literally talking to a friend who used it to help their kid, you know schools back in session They use it to help their kid solve some math problems, right something that they didn't understand easy to help with that So that's one kind of area of it But when it comes to the enterprise, okay now we're talking about something completely different now.
00:05:25.081 --> 00:05:40.076
We're in the enterprise space Sometimes I think that organizations seem to think that, it's just as easy as helping my kid with their math problem on school.
00:05:40.076 --> 00:05:40.927
Is that correct?
00:05:40.927 --> 00:05:46.552
Is it really that easy when you're looking at implementing GNI in the enterprise?
00:05:46.598 --> 00:05:48.228
Gosh, that's such a loaded question.
00:05:48.228 --> 00:05:50.649
I'm gonna go with it depends.
00:05:50.790 --> 00:06:13.059
So it depends in that if you're trying to empower your teams to do simple tasks like math problems or maybe coding would be a good example where you want to enable your developers to develop JavaScript, HTML, CSS, Python, whatever languages that you are.
00:06:13.339 --> 00:06:16.494
Sure, they can go in, they can do prompt if they know exactly what they want.
00:06:16.494 --> 00:06:17.355
it in.
00:06:17.355 --> 00:06:26.622
But where it gets difficult, and this is the maybe piece of it is, is AI is only as good as your data and it's only as good as the context it has.
00:06:26.622 --> 00:06:40.132
So do you want your marketing team to be saying, know, chat GPT, create me an email for this product, but it can't create the email unless it knows what is your corporate tone of voice?
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What is your products and services?
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Who are you targeting?
00:06:43.704 --> 00:06:45.435
What's your target audience?
00:06:46.389 --> 00:06:55.617
emails have performed in the past really, really well, and can we use that information to dictate the email that's being created?
00:06:56.098 --> 00:07:07.146
yeah, mean, out of the box, sure, you can have a very rough version of generative content being created, but if you really want to unleash the power of it, you have to feed it data, you have to feed it context.
00:07:07.146 --> 00:07:19.088
And that's where kind of the magic happens with AI in deploying it to maybe non -technical folks who don't know that they need to feed it this context.
00:07:19.088 --> 00:07:33.422
So that's on the enterprise deployment team to be setting those guardrails around security and feeding it the context that's appropriate for the teams that are leveraging it.
00:07:34.048 --> 00:07:37.202
That's very insightful, right?
00:07:37.202 --> 00:07:54.478
So what I'm hearing is that, you know, while it can make your life easier, there's some work that has to go into it in order to get it to the place where now you can start, you know, leveraging it across the enterprise.
00:07:54.478 --> 00:07:57.990
I think of it more of like a scale of usefulness.
00:07:57.990 --> 00:08:23.630
if you don't do any pre -work and you don't do anything, yes, you can use it, but it's not going to be as helpful if you do the pre -work and you fix the underlining data, if there's data issues and you can feed it the context of what is your corporate tone of voice and, how do you word things and how do you position your products and services and, know, what would a, an upsell look like in your customer buying journey?
00:08:23.630 --> 00:08:23.923
so.
00:08:23.923 --> 00:08:28.055
So it just becomes more useful the more time and effort you put into it.
00:08:28.055 --> 00:08:30.896
But that's the great thing about AI is it is useful out of the gate.
00:08:30.896 --> 00:08:39.219
It just might not be as useful if you were to have a little bit more of a thoughtful approach to deploying it within your organization.
00:08:39.754 --> 00:08:41.509
Yeah, I love that.
00:08:41.899 --> 00:08:49.623
You really need to have a thoughtful approach to how you're going to deploy it in your organization in order for you to get the biggest bang for your buck.
00:08:49.623 --> 00:08:52.614
That makes a lot of sense and that's pretty smart.
00:08:52.913 --> 00:09:06.000
What other challenges do you see when you're talking to organizations about taking the next steps with GEN .AI and how they can use it to automate their business processes?
00:09:06.000 --> 00:09:12.104
What other challenges, you mentioned data is a challenge, but what other challenges do you see organizations running into?
00:09:12.104 --> 00:09:13.768
our routine link.
00:09:14.500 --> 00:09:16.374
Yeah, I mean, I think...
00:09:17.039 --> 00:09:21.101
The main challenge is it can do so much.
00:09:21.381 --> 00:09:28.745
It, can be difficult to whittle that down to what the most impactful thing it can do for your organization.
00:09:28.745 --> 00:09:32.005
You're to have a lot of different teams that do a lot of different things.
00:09:32.027 --> 00:09:33.667
they operate differently.
00:09:33.667 --> 00:09:40.980
So figuring out where you start is as oftentimes a challenge in that it can be actually so difficult that people just don't even start.
00:09:40.980 --> 00:09:46.543
Like they don't even do anything because they're like, gosh, this is going to be way more difficult than what we can handle.
00:09:46.543 --> 00:09:47.293
So.
00:09:47.466 --> 00:09:59.485
I guess my advice there is like start simple, start with a beta test group, maybe start with a specific team that maybe is conducive to change more than other departments, right?
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So like your legal department, your HR department, your maybe corporate IT department, they might be a little bit slower to adapt than maybe your marketing team, right?
00:10:08.403 --> 00:10:16.187
Your marketing team around content generation, they might be more quick to adapt to testing it out.
00:10:16.782 --> 00:10:18.912
But also security, right?
00:10:18.912 --> 00:10:20.381
So you do need a loop in your IT team.
00:10:20.381 --> 00:10:22.731
You need to go through the checks and balances.
00:10:22.731 --> 00:10:30.782
If you're SOC 2 compliant organization, you need to make sure that you're adhering to all of the controls that you've agreed upon for your audits.
00:10:31.142 --> 00:10:35.782
But yeah, would say like starting small because that can be very intimidating.
00:10:35.782 --> 00:10:38.922
And then also we talked a little bit about data, right?
00:10:38.922 --> 00:10:46.658
So data and fixing the data is it can be a big blocker to launching something like AI in an organization.
00:10:46.658 --> 00:10:48.101
it's just not clean.
00:10:48.586 --> 00:10:50.798
Yeah, that makes a lot of sense.
00:10:50.798 --> 00:10:52.438
That's very practical.
00:10:52.438 --> 00:11:01.663
Now, for our audience, one of the things that we want to do is help them understand the tools and the platforms that are out there.
00:11:01.784 --> 00:11:09.568
Can you explain to our audience what exactly does Tray .ai do?
00:11:09.881 --> 00:11:13.212
Yeah, so Trey is an integration platform.
00:11:13.212 --> 00:11:18.054
It's an Ipass solution, so integration platform.
00:11:18.436 --> 00:11:20.115
And then it connects your tools.
00:11:20.115 --> 00:11:29.019
So just to give you a very simple use case, let's say you have Salesforce or HubSpot or whatever your CRM choice is.
00:11:29.100 --> 00:11:37.264
And let's say if you have a HubSpot, a deal goes closed one, or if you have Salesforce, an opportunity goes closed one, and you want to do something with that information.
00:11:37.264 --> 00:11:41.538
Let's say you want to, one, give a little shout out to your team.
00:11:41.538 --> 00:11:47.764
You want to send like a Slack notification or a Teams notification to be like, hey, so and so closed the deal.
00:11:47.764 --> 00:11:48.674
It was like this.
00:11:48.674 --> 00:11:50.255
This is a really good opportunity for the team.
00:11:50.255 --> 00:11:53.668
Get some corporate communications out.
00:11:53.668 --> 00:12:00.614
But then also maybe what you want to do is you also want to take the opportunity and impact the customer experience, right?
00:12:00.614 --> 00:12:02.134
So you want to send out an invoice.
00:12:02.134 --> 00:12:03.265
So you need to create invoices.
00:12:03.265 --> 00:12:08.158
You need to share that information with QuickBooks or NetSpeed or whatever your ERP is.
00:12:08.559 --> 00:12:09.734
And then you need to communicate.
00:12:09.734 --> 00:12:12.215
financing you need to make sure that there's follow -up.
00:12:12.216 --> 00:12:14.498
Make sure they pay their bill on time, right?
00:12:14.498 --> 00:12:21.984
So, Trey is kind of the platform behind the scenes that orchestrates the communication between all of your tools.
00:12:21.984 --> 00:12:33.903
And that's just one use case on maybe a quote to cash type, use case, but there's like tons out there from marketing, having an ingestion for leads, right?
00:12:33.903 --> 00:12:40.006
So you might use, Google or Facebook or LinkedIn lead gen forms for lead capture.
00:12:40.250 --> 00:12:43.711
but getting that connected into your tech stack easily is really difficult.
00:12:43.711 --> 00:12:49.634
Normally you have to go into LinkedIn, you have to log in, you have to export a CSV, you have to import that CSV into your CRM.
00:12:49.634 --> 00:12:51.676
It can be very time consuming.
00:12:51.676 --> 00:12:55.506
Trey basically handles the automation from start to finish.
00:12:55.506 --> 00:13:05.022
So you can plug LinkedIn into Trey, plug your CRM into Trey, and when someone fills out a form, it's milliseconds of that record getting into wherever it needs to go.
00:13:05.600 --> 00:13:07.442
Well, that's pretty cool.
00:13:07.442 --> 00:13:14.725
Now, you did mention early on that Trey's gone through a rebrand focusing on AI.
00:13:14.725 --> 00:13:28.248
Can you tell us about some of the features that Trey now has from a generative AI perspective and how that might work within one of these workflows or for an organization?
00:13:28.248 --> 00:13:35.322
Sure, yeah, well, So we've released a series of connectors called Merlin connectors.
00:13:35.322 --> 00:13:39.604
So for an example, we have Merlin guardian, which tokenizes your data.
00:13:39.604 --> 00:13:59.371
So if you're looking to do something with, let's say, open AI, let's say you want to, write an email for a specific person, you know, you have their name, you have their title, you have their, address information and you deploy something to marketing for them to use.
00:13:59.371 --> 00:14:00.735
They might actually put all.
00:14:00.735 --> 00:14:09.091
that information into their prompt and say, Hey, write an email for, and then they put in sensitive information, PII sensitive information.
00:14:09.393 --> 00:14:15.386
Merlin guardian, essentially, if you were to put the build this on the trade platform, you can push it through Merlin guardian.
00:14:15.386 --> 00:14:17.458
It'll actually tokenize that information.
00:14:17.458 --> 00:14:22.582
if it sees, marketing team put in a name, they put in a last name, they put in an email address.
00:14:22.582 --> 00:14:24.323
We do not want to send that to open AI.
00:14:24.323 --> 00:14:31.089
So Merlin will essentially tokenize it, send the tokenized version of their prompts to open AI.
00:14:31.523 --> 00:14:34.583
open AI will have the context of what those tokens are, right?
00:14:34.583 --> 00:14:37.947
It'll know it's a name, but not know what name it is.
00:14:38.347 --> 00:14:41.691
It can write that email, come back with the response and tray.
00:14:41.691 --> 00:14:45.923
And then Merlin guardian has another feature where it de tokenizes it.
00:14:45.923 --> 00:14:48.475
So it converts it back to that personalization.
00:14:48.475 --> 00:14:52.548
And then it sends that de tokenized version back to your marketing team.
00:14:52.548 --> 00:15:00.437
So they don't actually know that their sensitive information, they fed into the platform, never actually made it to open AI.
00:15:00.437 --> 00:15:04.051
but the experience is seamless, right?
00:15:04.051 --> 00:15:05.253
It doesn't throw an error message.
00:15:05.253 --> 00:15:07.835
It doesn't say, don't give sensitive information.
00:15:07.835 --> 00:15:17.774
It is all handled on the backend securely, open AI, never got access to it, but you can deploy it in a way where you've set those security guardrails for marketing.
00:15:18.240 --> 00:15:33.705
That is an amazing piece of functionality, especially now in this world where we have to be very cognizant about security and privacy and data, what we're sharing and how we're sharing it.
00:15:33.705 --> 00:15:42.307
That's an awesome feature and functionality that can be built into your workflows by using Trey, right?
00:15:42.307 --> 00:15:46.389
So when you think about...
00:15:48.150 --> 00:15:56.452
just when you look out into the space in general and you see what generative AI can do and there's so many things that it can do.
00:15:56.452 --> 00:16:04.604
What are some features or capabilities that you believe are missing in some of the tools in the enterprise space?
00:16:06.576 --> 00:16:13.796
Yeah, I would say security might be a concern.
00:16:13.796 --> 00:16:16.557
I think that folks are moving very fast.
00:16:16.557 --> 00:16:24.090
And that's the reason why we've been built the example I told you to begin with is because folks are moving so quick.
00:16:24.090 --> 00:16:31.332
It's difficult to trust that some of these smaller AI shops who might do the thing you need it to really, really well.
00:16:31.692 --> 00:16:33.134
But they're very small team, right?
00:16:33.134 --> 00:16:34.735
They may not be sock to compliant.
00:16:34.735 --> 00:16:36.335
They might not be HIPAA compliant.
00:16:36.335 --> 00:16:38.956
They might not be GDPR compliant.
00:16:38.956 --> 00:16:45.273
So you know, making sure that you've built those controls on your end versus depending them to is big.
00:16:45.273 --> 00:16:55.517
And then the other big one that I would say is, you know, early on in this conversation, we talked about like a knowledge base or context around prompt building.
00:16:55.998 --> 00:17:07.973
You know, unless you've built your own chatbot on OpenAI, there is an opportunity to upload knowledge base articles, but they don't make it very easy to add that additional context.
00:17:07.973 --> 00:17:35.037
So I think that that could be bit of a gap right now, but everyone is scrambling to fix that so Yeah, I'd say that like security and then also the context piece of prompt generation Yeah, those are two great things that need to be addressed, but I think exactly like you said, like those are things that people are scrambling to fix as quickly as they can.
00:17:35.737 --> 00:17:39.597
So what about the future?
00:17:39.597 --> 00:17:44.220
What do you think some cool things are gonna come in the near future?
00:17:44.220 --> 00:17:47.941
What excites you about, you know, what's next?
00:17:48.068 --> 00:17:49.118
Yeah.
00:17:50.076 --> 00:17:51.715
It's interesting question.
00:17:51.715 --> 00:18:00.625
It's hard to predict what the future is going to be, but we are definitely at the, exponential growth AI journey, right?
00:18:00.625 --> 00:18:06.096
I mean, it, when did open AI release chat GPT like late 2022.
00:18:06.096 --> 00:18:12.756
And here we are only a couple of years later and almost every company, every conference is talking about AI.
00:18:12.756 --> 00:18:17.175
just within a couple of years, it's completely reshaped the landscape.
00:18:17.215 --> 00:18:26.221
So another two years, I don't know what that's going to look but I know that like you mentioned earlier, it's level setting the playing field.
00:18:26.662 --> 00:18:30.482
So the way that we do work is going to be different.
00:18:30.482 --> 00:18:34.755
So if you're a developer, your, your day -to -day workload is going to look different.
00:18:34.755 --> 00:18:39.978
It's going to be the co -pilot of every employee in every organization.
00:18:39.978 --> 00:18:44.628
And you know, here in Dallas, we actually have McDonald's that's fully autonomous.
00:18:45.049 --> 00:18:47.431
There is no person who hands you your food.
00:18:47.431 --> 00:18:52.786
There actually is no person you can even talk to when you go into the McDonald's.
00:18:52.786 --> 00:19:01.791
So if you want to look at a weird AI future, take a look at the McDonald's in Fort Worth because it's completely autonomous.
00:19:01.791 --> 00:19:04.223
Now that's an extreme example.
00:19:04.223 --> 00:19:10.897
I don't think every restaurant in America or the world is going to be handled by robots in the near future.
00:19:12.429 --> 00:19:24.234
It's definitely going to be ingrained and there's going to be basically HR onboarding courses for, you know, there's gonna be like risk mitigation and there's gonna be like your AI certifications.
00:19:24.234 --> 00:19:26.705
So it's, going to get interesting real quick.
00:19:27.050 --> 00:19:33.491
That is very cool to think about going to McDonald's and not seeing a person at all.
00:19:33.491 --> 00:19:36.738
And honestly, some of the McDonald's I've been through, I'm excited.
00:19:36.738 --> 00:19:40.663
Like, can you please get back to McDonald's next to my house?
00:19:41.038 --> 00:19:44.053
You're like, I want my burger and french fries as quickly as possible.
00:19:44.053 --> 00:19:46.425
I don't care if it's a robot that gives it to me.
00:19:48.068 --> 00:19:48.930
Yeah.
00:19:49.090 --> 00:19:49.810
Yeah.
00:19:50.242 --> 00:19:52.615
man, this has been a fun conversation.
00:19:52.615 --> 00:19:54.498
Hey, you know what?
00:19:54.960 --> 00:20:00.467
Can you tell us a little bit about what you do when you're not geeking out over Gen .ai?
00:20:00.467 --> 00:20:02.328
What type of fun things you like to do?
00:20:02.349 --> 00:20:02.611
man.
00:20:02.611 --> 00:20:02.971
All right.
00:20:02.971 --> 00:20:10.099
So I'm I love rockhounding and I don't know if you know what that is, but basically it's what you say.
00:20:10.099 --> 00:20:10.912
No, me about it.
00:20:10.912 --> 00:20:11.313
want to know.
00:20:11.313 --> 00:20:12.028
I don't know.
00:20:12.028 --> 00:20:27.683
So rock counting essentially is you basically looking for like special like certain minerals like gold, silver, pyrite, like quartz, like, you know, you kind of like for like fun rocks and gemstones kind of out in nature.
00:20:27.683 --> 00:20:29.095
So that's like that's really fun.
00:20:29.095 --> 00:20:30.775
I'm like big into nature.
00:20:51.690 --> 00:20:53.992
that is very, very cool.
00:23:58.593 --> 00:24:04.056
Steven, thanks for joining us on the show today and thanks everybody for tuning in to Swimming with Sharks.
00:24:04.056 --> 00:24:12.299
And please check us out past, post, future episodes at manobyte.com forward slash SWS for Swimming with Sharks.
00:24:12.299 --> 00:24:14.770
Thanks everybody and we'll see you the next time.
