I'm Koji Ikeda . I am one of the software analysts here at Bank of America. I am absolutely thrilled to have Intapp here today. We have a special guest, Thad Jampol, that you are the Co-Founder and Chief Product Officer , and you don't get out on the road all that much. Happy and thrilled to be able to do this with you. We also have Dave Morton, CFO. Thanks for joining us.
Thanks for having us.
Yeah, absolutely. Intapp, vertical software business for professional services and financial services. I'm a big fan. I think what you guys do out there is great for all the different professional services and financial services organizations out there. Maybe for those in the room that are unfamiliar with Intapp and those on the webcast that are unfamiliar with Intapp, maybe from a high level, what is the background of Intapp? You've been there for a long time. Tell us about the kind of the origin story there. What are you guys doing right now? What is the opportunity that you're addressing?
Absolutely. No, thank you again for having us. It's great to be here.
Yeah.
For those that don't know, as you said, Intapp is a vertical industry cloud company focused on the financial and professional services industries. We take a lot of inspiration from companies like Veeva, who build industry clouds for very specific, specialized industries. Our end markets are the large partnerships. It is the large law firms, large accounting firms, large consulting, large investment banks, private capital firms, and real asset investors. These are the industries that really drive the world's deals. It is a surprisingly large industry, about 3% of the global economy. These are partnerships. The business model, the operating requirements, and the culture are just extremely distinct from other industries. It is one of the reasons that we believe this industry has been underserved and overlooked by Silicon Valley historically.
You think about it, these organizations make deep foundational promises of trust and confidentiality to their clients. They hold a lot of the world's secrets. They are regulated in a way that no market is. They do not sell physical products or widgets or SKUs off of a price sheet. They compete based on knowledge, intellectual capital, and these longstanding relationships. Structurally, they are mainly made up of professionals organized not by the traditional functional department, but by area of expertise. We look at this and say, look, these are very, very profound differences. These industries, because of that, require hyper-specialized solutions and software, which is where Intapp comes in. Intapp offers a very broad operating platform purpose-built for this industry. It is made up of I'm the product guy. I talk about the product.
It's really made up of four main product lines. We have a solution that helps firms win new business by managing the complex web of deals and relationships and clients, investors, and intermediaries. We have a solution that helps firms execute and deliver on that work collaboratively and efficiently. We have a solution that it's a work-to-cash solution to ensure that these firms get paid for all their work and that they could distribute maximum profits back out to the partners every year. We have a compliance suite that sits underneath everything to help them navigate this sort of complex set of compliance and regulatory obligations that they have.
Got it. Sounds like you got, I mean, I know you guys do. But it sounds like you guys have a front office and back office platform for industries that are data-driven, trust-driven, knowledge-driven.
Absolutely.
Which makes it difficult for horizontal vendors to be there, not only from a, let me ask you that question. I remember during the IPO period, we talked a lot about Salesforce. Salesforce can do it. They have a horizontal solution that can do it, but maybe it is not the best solution from a feature standpoint and maybe from a cost standpoint. What are some of those things that the platform can do that makes it better, faster, more cost-effective than something like a horizontal platform?
Yeah, absolutely. Not coincidentally, you're going to see some dovetailing with the difference between AI from the horizontals versus vertical AI. The answer is sort of the same. You come back down to starting with the data. Horizontal systems are really built for the manufacturing and retail worlds. They have very linear processes. You think about, just as an example, a typical transaction. There's a buyer, there's a seller, there's an asset or a SKU, and there's a bunch of people around that on each side. In this industry, there's co-investors, there's referring banks, there's lending syndicates, there's diligence teams, there's advising counsel, there's opposing counsel. All of these different stakeholders are critical to how these firms operate. You need to be able to capture that so that you can reflect it back.
I would put data as a big one. I think a second one is compliance. Again, I was talking about this. Compliance is existential to these organizations. Everything these professionals do is subject to conflicts or confidentiality or regulatory scrutiny or professional and ethical responsibility rules. It is not a check-the-box thing. It is not an after-the-fact bolt-on. Again, I am probably jumping the gun here, but in AI, this is one of the big obstacles and encumbrances of this first wave of AI because it does not really think about the very profound compliance requirements and obligations that there is for client-second. Thirdly, we are so intimate with the way that these workflows and processes happened as part of our industry orientation that that is a big one.
Understanding how are you augmenting and supporting these hyper-busy professionals in the way that they work and within the tools that they work. Each of those three, whether it's the data, the compliance obligations, or sort of the workflows and processes, we just feel like we have a big advantage being so close to this market.
I know I've probably told John, your CEO, this before, but I'm still waiting for the equity research CRM tool from you guys. I know that you guys do a lot of investment banking and kind of deal management with the DealCloud. Yeah, when are you guys going to come out with the research product? Maybe a question for Dave. How do we think about the demand environment, maybe split between broadly professional services and financial services? What does it look like today? How are you thinking about it for the future?
Yeah. I mean, I think our bigger context and some of the moves we made earlier this year with our go-to-market and looking at where some of the bigger opportunities were coming, before we kind of weighted everything the same. Whereas what we talked about at our investor day, we saw over 70% of our SAM, TAM coming from the top 2,000-plus clients. That is where we kind of started over-indexing on that go-to-market motion. As a result, we have continued to see bigger pipe, win rates, a lot of strong deals come through that whole ecosystem, not only for this year, but then looking at this quarter is what we talked about not only a couple months and a half ago on our earnings call, but then even into FY 2026. We are really excited about those opportunities.
We'll be excited to talk to you all here at the end of the quarter as well as what that implies specifically for FY 26. We believe everything that we've done, as well as the rate of pace of innovation that Thad and his organization has brought on, we feel that we're positioned really nicely for how this company is continuing to deliver.
OK. I'm going to start macro and kind of bring it in slowly down to vertical AI, but just starting macro. The demand environment within the categories that you're operating in, professional services, financial services, has been proven to be pretty resilient. Kind of IPO time through the COVID ups and downs, the recessionary fears. Even today, you guys have been pretty darn good. What is it specifically about the professional services and financial services end market that really creates this nice end market?
I think it's a few things. I think first, we're very fortunate to have an incredibly stable end market. Even in sort of the down markets and as it fluctuates, you're going to pay your lawyers. You're going to pay your tax professionals. You're going to pay your auditors. When you think about the private capital partners, they're getting paid at 2% out of their management fees, but it's from a fund that has a five-, seven-, nine-year lifespan. It's long enough where they can weather whatever ups and downs there are there. I think secondly, there's just an inherent diversification in our markets. A lot of these firms are made up of sub-businesses of different practices or different service lines or different asset classes and strategies. We have seen in the past a shift in go-to-market and investment.
In a down market, you'll see accounting firms maybe move from advisory stronger into the audit side. You'll see firms move from some of the cyclical transaction work into more countercyclical litigation, restructuring, bankruptcy work. Even the private equity, private capital firms, they're inherently diversified because they have portfolios of different assets underneath that. So there's diversification. Lastly, I'd call out, and perhaps counterintuitively, and we've seen this going through the 2008, 2009 financial crisis and COVID, that firms, when you take the foot off the pedal a little bit in the go, go, go, deal, deal, deal, deal side, they have a moment to go enhance their operation, to go make some investments that they've wanted to do for a while. They'll spend in the areas that they deem strategic, even while they're reducing and cutting some of the ancillary areas.
We have been very fortunate to be deemed within those strategic categories. We have done some of our biggest deals in the depths of financial crisis or COVID.
Let's move to AI.
OK.
We'll start macro on AI.
OK.
Tell me a little bit about why vertical AI is maybe differentiated from horizontal broad AI.
Yeah. I touched on some of this earlier, but we talk to our customers a lot. There are two particular moments that I think are really illustrative. In the beginning of the year, we have a CIO advisory board that we host in Redmond together with Microsoft. We invite a few dozen of the most influential, innovative CIOs, and we talk there. Following that, we have an event we call the Ambassador Event, where we invite some of our biggest power users. When we had these sessions last year, 2024, there was a lot of optimism on the first wave of this generative AI, as we were talking about, mostly from the horizontals because they were early out of the gates. They were piloting. They were trialing it. When we got together this year, the tenor was very different.
That they felt that while a lot of this technology had cool aspects to it that had potential, it ultimately did not deliver the outcomes they were looking for. I do think it comes down to those three things. At least this is what I have been told, is that it is not using the firm's data. These models are so powerful, and they are incredibly innovative, and we are excited about them. At their core, they are inherently generic. They are raw technology trained from information across the broad internet. That is part of its power. The broad internet does not understand the precision of the deals and the transactions and litigations that these professionals work on every day. It did not use their data.
Secondly, a lot of the general counsel's office and compliance offices just put the brakes on these when they started panicking about the oversharing risks that these co-pilots and models might introduce into these firms that have MNPI. They have PII. They have trade secrets. They have comp information all over the place. A lot of these first wave of innovations did not really consider the implications of that. Thirdly, and I think this is probably the biggest one, is the professionals never really adopted en masse the tools that came in. I think this is because these initial waves of generative AI, they are separate panes or frames that sit on the side of your screen. They are blank with a blinking cursor. We joke that it feels like MS-DOS. They just sit there.
They ask you to type in something inspirational or something really important, and you get natural language back out. It puts this incredible cognitive burden on these very, very busy professionals. It is asking them to context shift away from the other applications and working on the deals and working with their clients and fighting their competitors and developing prospects to jump over to this other screen that is blank, enter something, get the results, find a way back into your application and your workflow. Those areas, I think people were very suspect of. They have shifted their focus now much more practically into more vertical AI, to your point.
They are really focusing on grounding the data within the firm's proprietary information, having a compliance confidentiality-first offering, and ensuring that the AI is manifested directly within the workflows and the processes and the productivity tools that the professionals are using every day.
Is the thought process here within your industries a blinking cursor, ChatGPT screen? How can I help you is not, how can I help you, the cognitive burden that you're facing?
Yeah.
How do you bridge that from with what the offerings that you have that were something where I'm afraid to put something in here because I don't know what's going to happen with the sensitive data and compliance and everything? How do you make that easier, more digestible for you?
I'll answer that in two ways, starting with the end of that, which is the how do you have confidence, trust that all this AI is going to be compliant and work with what you're doing? The first thing to do is to ensure that all of your information is protected. It sounds fairly straightforward to do. All of these innovations from Microsoft and the productivity tools, which are incredible, all the Teams and the OneDrive and all the M365, they're so collaborative. They make it so easy to unintentionally do something that reveals or shares sensitive information. You need the counter to that. That's an area that Microsoft and Intapp have worked together on. It's in our mutual interest. We have a confidentiality solution that we call Walls for AI.
We launched it on stage last, oh, one of our big shows. We had the head of Copilot on stage with us. It really looks at the overall sort of data landscape and identifies where sensitive information is. Is it protected? Is it not protected? The Walls product is a centralized place that can enforce the right confidentiality policies across all the different systems. That is sort of the first part of your question. The second part of your question is, how do we bridge this gap from the blinking cursor? This is where it gets really exciting. If you look across a lot of our applications, I will pick a few out of the air, you look at Time. We have a Time product that helps essentially lawyers track their time and ensure that they get the bill out of this.
We use AI to capture it. We could embed right there in the application. We can tell you all the time that you worked. We can tell you what your timesheet is. You can now talk to it. It will build out a narrative. The AI will figure out if that narrative is actually a good narrative. If it conflicts or violates any commitments you've made to clients, it'll tell you. It will suggest changes to it. It's right there in the application. You don't need to go anywhere else. Our conflicts of interest product. A lot of these firms, before bringing on a new piece of work, you have to go through a fairly rigorous process to ensure that you're not representing somewhere the opposite side of a deal or a litigation or something like that.
We have AI embedded right there that will say, based upon the way this firm operates and what we've seen from other organizations where risks, here are the areas that you might want to double-click on and spend some additional time because we think there might be something risk here. Within DealCloud, within our product that allows you to go win more business, we can embed right there capabilities allowing you to go see all of these exciting opportunities that you might invest in based upon your historical investment. The list goes on and on. The point to take away here is that it's embedded directly within these applications in context at the moment that you need it.
It sounds like you guys are doing all the right things with AI. I'm going to put my more balanced hat on for you again. What's the catalyst for your customers to adopt this stuff? What are they waiting for? Are they still waiting? I mean, how do we think about what gets them to start really buying this stuff?
Yeah. Surprisingly, or maybe not surprisingly, a lot of people here probably work at a firm within this industry that these industries have been a little slower in the core of digitalization and the digital transformation. A lot of the trends that we've seen of moving to the cloud in other industries are still happening right now in this market. We're working with a ton of organizations and helping them move their older on-premises, in-house built tools over to our modern cloud and SaaS solutions. That is a big demand generator for us. On top of that, what really gets them excited is that that is an enabler of generative AI because you have to be in the cloud really in order to take advantage of this.
They, like we, are huge believers and very bullish on the potential transformative opportunities of generative AI and the way you operate and compete. We are seeing this opportunity and this demand generation for both the cloud and then the generative AI on top of that. Compliance is the third one, again, very existential to this industry. They want to get it right. They also see the yin and the yang between having the right compliance and confidentiality underpinnings allowing you to bring in all of this generative AI and be able to take advantage of it in a safe and trusted way.
Throughout your answers, Thad, you mentioned Microsoft. You mentioned Seattle.
Yeah.
I know you guys have a big partnership with them.
We do.
Maybe for those in the audience and those in the webcast that aren't unfamiliar with your partnership with Microsoft, tell us about it. How deep is it? Where is that going in the future?
Sure. Yeah. So it's about three years in right now. There are many pieces to it, but I would call out really two big pieces. The one that gets our clients most excited is the co-innovation side of this. Every year that has gone by, we've gotten closer and closer to the engineering teams. We're talking to them regularly. We're co-innovating in a number of areas. I talked about Walls for AI. That's a very important one. We really are looking to make sure that all of our innovations, again, to this blinking cursor point, meet professionals where they are today. They're not just requiring this big context shift. One of the places where a lot of professionals are in our industry is in M365 and the Office suite.
We have a lot of initiatives together with Microsoft to be able to natively plug right into Teams or right into Copilot. We have a lot of progress there. We share a lot of it in our big Red meeting. That's really exciting. We're also really excited about the go-to-market and the co-selling together with that. Microsoft has these Microsoft Azure minimum commitment agreements, spend agreements. They're called MAC agreements. What's really, really great because we are in the top tier of their partnership is that firms will get dollar-for-dollar credit against their committed Microsoft spend buying Intapp products. We actually just had a really, really big account who was kind of on the fence between buying on Buy and DealCloud say, oh, I could use the MAC spend because your product is in the Azure marketplace.
It's essentially free for me if you've got that dangling out there. In certain situations, it's been a real catalyst and an accelerant of deals. All of these firms and all of the industries that we serve are very heavy Microsoft users.
OK. OK. Tell me about Amplify, kind of a big event that you guys had, lots of big announcements.
Yeah. Yeah.
I watched it, but I'd love to hear kind of your view and a few of the big announcements.
We're really excited by that. Amplify is the name of our big annual customer event. It's very heavily product-oriented. We share a lot of R&D, a lot of our innovation. We make a number of big product and SKU announcements there. It's held in New York. Last Amplify in 2024, we announced our Intapp Assist brand, which is our generative AI brand. We launched our Assist for DealCloud SKU. We're really, really excited about the uptake that's had in the market. We followed that up with a number of other assists in our product line SKUs. This Amplify, which we just had in February, we doubled down on AI. We made a number of, I think, really, really exciting announcements that went over really, really well with our market. Just to give you a few examples, we announced Assist Smart Tags.
Assist Smart Tags uses AI to analyze all the conversations and call notes and meeting transcripts that happen throughout the day within these organizations and then to be able to identify the interesting nuggets of intelligence and then bring them to the relevant stakeholders instantly there. This is critical because think about how many of these conversations happen every day. These are relationship-based businesses. The number of calls and meetings, I mean, it's hundreds or thousands every day. The insights, the competitive intelligence, the opportunities that get buried in this unstructured data, these call notes that just sit in the system and never see the light of day, it's incredible. What Smart Tags is able to do is go in there and find it and bring it to the right professionals so that they can be smarter in front of their prospects.
They can find more opportunities. They can have that extra advantage against their competitors. That is Smart Tags. We announced Assist Prompt Studio. Prompt Studio allows you to get very granular in the configuration of prompts and how generative AI is surfaced within the application. A great example that I just heard is there was a firm. This was a sort of a well-known middle-market private equity firm. They really wanted to focus on the reciprocity of their advisors, meaning they are spending a lot of fees with their advisors. They want to make sure their advisors are bringing them leads back. They configured the prompt. I just checked it earlier. They are able to say, show me right on the advisor's page when I load it up in DealCloud, I want to know the total amount of fees that we have spent with this advisor.
I want to know how many sell-side leads they brought me. I want to know how many of those leads converted opportunities. I want to know how many of those opportunities we actually worked. I want the AI to summarize the health of our relationship with this advisor. It's a great example of what Prompt Studio can do. We've also seen firms who will want their growth equity partners to really see different information highlighted about companies, so Cap tables, Growth rate, CAGRs. They'll want to see the private credit team. They're going to be more interested in EBITDA and free cash flows and debt service credit ratios, those types of things. That's Prompt Studio. We announced Assist Origination. This was a big one for us. Assist Origination uses AI to go find and source the best new opportunities for a fund or a firm.
There is $3.7 trillion of dry powder that these firms have to go commit and deploy. We think the winners are going to be the ones who are most able to go directly source the best new platform acquisition or add-on acquisition faster than their peers. The magic of this is the data. We bring together all of this private company information from across the internet that we use Delphi, a company we acquired a few years ago, to do. We combine that with a lot of firmographic data feeds that firms use through third-party subscriptions and then firms' proprietary data itself within DealCloud. Have I met with this company? Have I done workups for models? Have I made bids? It is all surfaced directly in DealCloud.
It is just right out of the bat integrated with all your workflows, your pipeline, your Monday morning meetings, your investor committee preparation. You get this incredible force multiplication effect of coverage, sourcing BD teams of 25-30, being able to do the work of 40 or 45. We had a Time for AI. I talked about that. We had Walls for AI. I talked about that. There was just a lot of AI top to bottom. We introduced about a half dozen additional monetized AI SKUs.
Wow. Just listening to that, I would love to have Assist Smart Tags for equity research. If you could do that, we would love that. Throw that in there with equity research CRM.
I didn't know this was a pipeline generation event.
That all sounds great. How are you guys differentiated? Or how are you going to keep this differentiated from the competition? I think about two things. One, clearly, professional services, financial services, good growth. I mean, you guys show great growth in your SaaS. SaaS numbers, Cloud AR, I mean, good numbers there. I always fear that people will, the horizontal vendors might recognize this and come after you. Then it becomes product, right? Product will win. Talk to me a little bit about why your product is differentiated today and how is it going to continue to be differentiated.
It is a great bookend question. I opened with sort of the company. You had asked about some of the company's beginnings. When we started the company, we bootstrapped it. We got into law firms very quickly. The law firm that was doing our patent work looked at our product and said, we should have this. We hired their CIO as our first seller. They brought us into a lot of this very insular self-referencing community. What was so serendipitous for us is that we were Middleware at that time. We were integration. We did not see just one application area. We really understood the whole application landscape. We understood, more importantly, the data model. That was the formative start of really building what we call the industry graph data model.
We think this is one of our big innovations and one of our long-term durable technology moats. It really reflects the way that these industries and professionals work. We can capture that information that the AI so desperately needs. I use that example of sort of a transaction. That's an example of that. Because of this industry graph data model, you could answer these big strategic questions, which are not new, by the way. They've been out there for a while, but no one's been able to really do this. It's sort of like, what business and what segments have I been most commercially successful so I could do more of that? Who in my network has the most influence or referred work to me so I could spend more time with them? Where are the best deals for my fund?
What is the likelihood of transaction? What is my best path of introduction so I have a first mover advantage? Where are the best follow-on work or cross-sell opportunities so I can develop my clients? All of that is based upon having this incredible Industry Graph data model, which I do not think one of these horizontals can do anytime soon.
Got it. Maybe the last question here. And Dave, I did not forget about you.
No worries.
I want to, you guys are clearly in an attractive growth category. And so as the CFO, how do you think about balancing growth versus profitability? I mean, you guys show great margin expansion. But does that continue forever? Do you invest more? How do you think about it?
Yeah. I mean, we've shown really good diligence and judiciousness. I think it goes back to the culture of being bootstrapped. This wasn't a company that was born out of endless funding and then all of a sudden had to get profitable here in the 2020s. We are very thoughtful. We'll continue to apply economics towards that and his organization to keep that competitive moat out there. We'll continue to drive productivity within our sales and marketing and absolute reductions in G&A. We've done things with our services model. There could be some room to go there. All things being considered, yeah, we really like how we framed up on 2025. We like our long-term model. We think it's within near reach than not. Almost time for another investor day, might you say. Yeah, we're just really excited about those opportunities.
When you think about some of the returns that we're getting both on the AI products we're doing, it's hard to say why we wouldn't continue to invest while also contributing to the bottom line.
Got it. Yeah. I like that near comment. We'll leave it at that. Thank you guys so much. Fun conversation. Thanks so much for coming.
Thank you very much, buddy.