My name is Marc Riddick. I'm Senior Analyst with Sidoti, and I thank you for joining the Sidoti Micro-Cap Virtual Conference this morning. Our presenting company is Mastech Digital. The ticker is MHH. Joining us today is Kannan Sugantharaman, Chief Financial Officer, and Deb Satpathy, Chief Growth Officer. Before we begin, a reminder that we will have time for Q&A following prepared remarks. If you'd like to ask a question, don't be shy. Just feel free to submit those at any time by clicking the prompt at the bottom of your screen. There's no need to wait until the end. With that, we can get started this morning. For those who are new to the Mastech Digital story, can we begin with a brief profile of the company? Thank you, Marc, and really pleased to be here. What I want to do in the next 12 minutes, along with Deb, my colleague, is to introduce you to the new Mastech. Not just who we are today, but the transformation we are in the middle of and why we believe it sets up to win in the AI-first economy. Let's get into it. Mastech Digital is a data and AI transformation firm. One mission: help enterprises become genuinely AI ready, not running pilots, not experimenting, actually ready at scale in production. We do that through two complementary capabilities, high-quality data and AI talent, specialized professionals embedded inside enterprise technology teams. That's about 67% of our revenues, our stable and recurring foundation. The data and AI services, where we design, build, and operate AI-ready infrastructure and agentic AI solutions. That's about 1/3 of our business today and our growth engine. 300+ enterprise customers, 1,500+ people, 10+ premium platform partnerships, over 100 proprietary assets and accelerators, NYSE-listed, minority-owned, six global delivery offices. That's a quick gist on what Mastech is today. Let me show you how the model is structured, right? Think of it as two engines, a talent engine, which is the foundation, stable, cash generative, and it opens doors inside enterprise accounts. Data and AI services, which is the growth engine, high margin, high differentiation, and it deepens the relationship. The two feed each other. What's distinctive is the depth behind services business. 20+ years of building trusted data foundation, master data management, governance, data quality, lineage. That institutional knowledge is genuinely hard to replicate. We bring industry expertise, the talent to execute, and platform capability to deliver at scale across Snowflake, Databricks, GCP, Azure, AWS, and Informatica. As data and AI services grows from the 33% towards a larger share of revenues, margins expand, and that mix shift is the investment thesis. Here is what's driving the urgency on that side of the business, right? Let me start with what we see as roadblocks to the AI journey. We have had hundreds of conversations with clients over the past 18 months. We typically see five roadblocks that come up every single time. One, the pilot to production gap. Enterprises can run proof of concepts experiments. They cannot scale it because the underlying infrastructure isn't enterprise-ready. Two, the talent gap. AI and agentic engineering workforce simply does not exist at scale, where the market needs. Three, governance, security, and regulatory pressure. Compliance is creating real friction. The time it takes to clear security reviews before any AI deployment can go live is significant. Four, the total cost of ownership. Token costs are spiking. It's the thing that everybody talks about today, and the ROI story, often it's not clear. We see this in our own operations. When we rolled out AI tools internally, the usage burns through monthly budgets within days. The appetite is real. The cost discipline is not yet in place. Number five, the whole adoption. Change management gets underestimated, and human machine interaction gaps, which is the thesis of what I'm going to talk about, don't show up until you are already production ready. Every one of those conversations came up with these pointers pretty repeatedly, and the framework we have built, which I will walk you through in the next two, three slides, is designed specifically to address those five areas. But first, it helps to understand where enterprises actually are in the journey. Here is the insight that shapes everything that we do. Entire enterprise stack was built over the last three decades around humans. A person reads the dashboard, interprets the report, clicks the button, makes the judgment call. All of our data, our systems, our applications, our business logic, it assumed a human in the loop who would handle ambiguity, decision-making. AI agents cannot. For an agentic tool to come in and to operate efficiently, data has to move from human-readable form to machine-consumable form. Systems need to be clean, callable interfaces, not login screens and dropdown menus. Business rules need to be explicit, not implicit, because agents cannot infer what's in the human mind, right? The shift we are helping clients make is this: From human-ready infrastructure to agent-ready ecosystem. The win is not about deploying the most agents. The win is making the entire stack legible to agents. That's the journey, and it's the one that we have built our entire framework around. Our framework is for doing this work, from moving from a human-readable to agentic-readable ecosystem, has four pillars, what we call the knowledge enterprise. Four pillars. First, data, which is our trust layer. Before we build any intelligence, we go into our existing applications, our existing environment, and recover the data lineage, the governance logic, the semantic relationships that are buried in your catalogs, glossaries, and your Confluence pages. We establish a clean, trusted foundation, and you cannot. Why? Because you can't build on sand, as they say, right? Without that data layer, you will not be able to do your agentic work. Second is the knowledge layer. This is where it gets interesting, the context layer. Once data is governed and trusted, we activate intelligence in stages. Think of it as like turning the lights on the room by room in your house, rather than just flipping one switch and hoping nothing blows out. Third, the orchestration layer, which is where the agents actually come in. This is where AI actually starts to do real work, semantic search, workflow automation, agents coordinating across systems through the MCP for protocols with governance enforced at each step. The fourth is the impact or the value layer. As a CFO, this is where I am more interested. Every investment gets tracked for business outcomes. What moved? What improved? That is what makes the program defensible and keeps it funded. Together, these are the four pillars taken together that drives what we call the fragmented AI experiments to a scaled, measured AI programs, where human provide the judgment, and agents execute with full contextual awareness. This is where I would want to also bring in Deb, but just before I do that, I just want to explain what is the mode of Mastech Digital in all of this. Any firm can put a framework that I showed to you on a slide. What they cannot do is to show up with the 20+ years of actually living inside enterprise data environments, the messy, the political, legacy-laden reality of how data works inside large organizations. That institutional knowledge shapes every engagement we run and is genuinely hard to replicate. We have converted that experience into repeatable assets. In the knowledge pillar, we have pre-built domain ontologies, a shared vocabulary for industries like retail, financial services, energy. We are not reinventing the semantic layer for every client. We are building these right now for major American convenience retail store, starting with their product catalog, but extending into stores, customer merchandising, and loyalty. Those ontologies become what we call the holy grail for the retailers who want to compete in the AI-first commerce world. We can talk about a case study on that as well. In the agents pillar, we built ADEPT, our proprietary framework for embedding agents into the existing client systems. The keyword is existing. We do not ask clients to rip and replace. We take proven off-the-shelf models, connect them through our standard interfaces, and embed them into production with governance, AgentOps, FinOps built in from day one. Not a pilot, specifically production. Which brings me to how we are commercially scaling this, and which is where I want to bring in Deb. Deb heads our growth office, and I want him to talk about our growth office and how are we building to compound. Deb, over to you. Yeah. Thanks, Kannan. To give you a little bit of view of what Kannan mentioned, as you heard, the need is real. As the AI adoption is increasing and would increase further, the problem is real as well, right? Keeping both the sides, the dichotomy scenario that we have here, we deliberately built the growth office keeping four foundational pillars. The first one. Now, these are all considered as a connected commercial engine. The first one is how do we reach out to more customers who have this need at the right time when they actually need it? That is all about the new customer acquisition, the new logo sales engine that we have built within the growth office. The second one is how do they know about us? How are we more visible to them? How do they know what things are we solving for other customers? That is where the performance marketing engine comes in so that we can spread the word, we can talk about it, they know about it, they see it for real. No one can solve this problem alone, and that's where partner ecosystem is very, very important. Mastech brings in things like ADEPT, its experience, its industry knowledge, being 40 years in the business, and the partners bring in the platforms, the foundational models, the things that they have been doing well on the software side. That is where we come together, and partner ecosystem is very important for us. Working with the likes of Snowflake, Databricks, deliberate investments with them, deliberate discussions on the roadmap and how do we solve for customers. The last one is large deals. Very important because as we, and I'll share some examples with you, as we keep on seeing these challenges that we are solving or the value that we are generating for customers, it's not a point solution anymore. It becomes a full stack problem, starting off from data, ending up with adoption as Kannan mentioned. That is formulating the large deal, not just by labor arbitration and things like that, but pure AI-led engineering solving for value. So that's the fourth pillar that we have formulated within growth office. To make this a little bit real, we announced a few deals in Q2. Let me take some examples for you on what are we actually solving. Because when you're solving for key personas in certain industries, that is what makes it more valuable. Let me take the first example. The first example is a scenario of where we are building AI foundation, but the focus is member experience. This is a customer in the payer space. Very known in their particular industry, have been solving for the Medicare/Medicaid space for a very long while. They have fragmented data. Everybody wanted to do AI, they wanted to embrace AI, but the data was fragmented. So the focus was very much on how do we build in an ecosystem, which as Kannan mentioned, AI-ready data foundation, so that the member experience can be made better. The focus is all around member experience. We built the foundation, focused on claims, pharmacy, members, PBMs, all these area, build the intelligent layer, getting the access to this particular data for faster decisions with the core goal of ensuring we are able to do faster claims, we are able to improve member satisfaction. So the focus is value and outcome, as Kannan mentioned. And how we build it is getting an AI-ready data foundation. If we focus on the second kind of scenario, this is solving for members in the payer space. When we start focusing around something that we do every day, say commerce, right? We buy stuff. This is for a convenience retail giant. How do we get personalized shopping experiences? Everybody knows what they want, but it's so difficult to find out exactly by searching. For this particular customer, we are building agentic commerce, which means whatever my intent is, I should be able to ask, I should be able to build my cart and move forward. This is where we are using our trademarked ADEPT platform to ensure that we are building agents right, we are moving things right, we are building it for scale. All focused around outcomes again. The digital conversion should increase. People should not move away from carts thinking that they bought something wrong or they put something in cart wrong. How do we bring in more accuracy with the core focus again, if I have an intent to buy, I should be able to action it much more seamlessly. This is the true solve with AI for personas, like members, like consumers like us in every day, which how we see, the kind of demand and the solutions that we are providing to our customers. Yep, I think we are good with explaining this. Marc, we can, if you have any questions for us. Absolutely. As a reminder, if you would like to submit questions, feel free to just click on the link at the bottom of your screen there. I wanted to start with, maybe you could talk a little bit about how you see the current market activity levels, for each of the segments, and how they may evolve through the remainder of the year? Yeah. Thanks, Marc. I'll take that. Let me take these, I would say one at a time, since the picture looks very different across the two segments, which is talent and data and AI. In talent, we continue to navigate two dynamics. The first is insourcing activity from one of our top 10 clients, which we expect to continue through the second half of 2026. The second is our own deliberate exit of low margin business, non-strategic positions. A choice, we are making to improve the quality of the segment. Both of these will continue to weigh in on the top line in the near term, and we are monitoring those situation closely. That's on the talent side. On the data and the AI side is where we have more encouraging signals. We grew 7.2% sequentially this quarter, our first quarter of sequential growth since 2024. We backed that up with a very strong bookings quarter, our second consecutive quarter at the same TCV levels of about $13 million-$14 million. We are also seeing this show up in the quality of our wins. The two case studies that Deb just spoke about are there. Alongside that, we have also launched what Deb explained on the growth office, and our formal partner program. Those are two things that Deb anyway presented. Taken together, we believe the momentum we are seeing in data and AI is real, and we are hopeful it translates into continued positive growth in the coming quarters, Marc. Excellent. For those who may not be aware, we are approaching the one-year anniversary of the launch of the EDGE initiative. EDGE is Efficiencies Driving Growth and Expansion. Can you share your current thoughts on what we are likely to see in the months ahead as you continue to execute on the initiative? Sure, Marc, and we speak a lot about that internally. EDGE is Efficiencies Driving Growth and Expansion. It was an initiative that was launched in the third quarter of 2025, and I would say from the outset, it was a deliberate program which had two clear phases. One was the efficiency phase, which we delivered last year, followed by the whole investment phase, which is where the growth and expansion is bound to happen. That is an investment cycle, nevertheless, right? I want to be clear that this was never only a cost-cutting exercise for its own sake, right? It was an optimization designed specifically to create the room to reinvest in those focus areas that we believe will define our future, which is into what we just spoke of, right, in terms of data and AI. In the first quarter of 2026, we saw the discipline of EDGE coming through. SG&A was almost down by $2 million or $8 million annually, annualized. Our intention from the start was to reinvest a substantial or almost all of those annualized savings back into our strategic priorities. The efficiency gains and the investment envelope are directly linked. EDGE has created the runway, and we are now in the deployment phase, right? That is broadly how we think about EDGE and the focus that we have going forward. Marc, back to you. Okay, great, and then wanted to sort of highlight. Mastech reported encouraging 2Q results just a couple of weeks ago. Maybe we can start with the data and AI segment, where, as you mentioned, that was the first quarter of sequential revenue growth, seen there since back in 2024, I believe. Can you share what you are seeing in this segment and potential drivers for continued growth? Sure. As I said, data and AI grew 7.2% sequentially this quarter, which was Q2. That growth is being driven by real demand we are seeing from enterprises. Broadly, we see two forces moving together in that market. The pace of AI innovation coming out of frontier labs like Anthropic and OpenAI, and the pace of AI adoption inside enterprises, particularly in the Global 2000 customers we work with, as they rotate the spend away from what we call the traditional technology investments and very specifically directed towards AI. A recent report from Zinnov, one of the advisory firms, suggests that as much as 30% of traditional IT spend could rotate in this direction over time, which is moving from the traditional investments into the AI super cycle. We believe that shift shows up in two areas, specifically. What we call the AI foundation, which is the data modernization work that underpins everything back to the whole layers. It was the data layer or the trust layer. Then the business transformation is where enterprises are building AI-driven use cases with measurable impact for that matter. Those are the two areas where we are seeing, I would say, our use cases coming up. That backdrop is translating into real bookings for us. This was our second consecutive quarter of strong bookings at almost the same level of about $13 million-$14 million in TCV compared to $9 million a year ago. Importantly, we are winning in more than one way, right? As Deb covered in his growth office and the deal wins pipelines that we are seeing, that is visible. Earlier this year, we won a strategic engagement with a large healthcare payer. He spoke about that in his use case, which was expanding on our master data management strength and expanded into data modernization project. This quarter, which is Q2, we have signed a strategic AI engagement directly with a large convenience store. Deb spoke about it as well. Seeing strength across both those parts, which is data and AI, gives us a real confidence in the durability of the demand. On potential drivers for continued growth, a few things clearly stand out. Almost half of our current data and AI pipeline comes from Global 2000 accounts, and we see that segment as a leading indicator to where the broader market is heading. We also launched Growth Office. Deb spoke about it, and internally, we are investing disproportionately into our own data, AI engineering, and modern data platform capabilities, which we believe is what will let us keep winning in these engagements at scale. Taken together, we believe the momentum in this segment is real, and we are very encouraged and positive by the trajectory we are on. Marc? Great. Deb, I want to say congratulations. First of all, congratulations on your new role as Chief Growth Officer for Mastech. Maybe for those who are not familiar or had the opportunity, can you take a moment to introduce yourself and share some initial thoughts on the role there? Absolutely, Marc. Thank you very much. Yes, 20 years in the business, all through on data and AI. Done more than 10-15 years in solving for patient journeys, very close to healthcare life sciences. That is why you would have seen my excitement while talking about the payer case study. Have been a founder of an AI product company in the past, solving on decision intelligence, which is now with a global SI who took over, and excited about the role. Has a lot of potential, a lot of deliberate investments, as you heard about from Kannan, and looking forward to it. Great. Now on the 2Q earnings call, there were certainly new business wins announced and discussed. Maybe you could talk a little bit about some of the catalysts behind those wins, as well as how new service offerings are playing a role in generating new business wins. Oh, absolutely. I think Kannan covered this a little bit in terms of we see the demand to be real, because many customers are not spending more, but they are moving or rotating a lot of their scenarios towards AI need and strategic work on AI, going beyond pilots to now focusing on harnessing AI and solving for value, right? Because it has proven that it can work, but how do we make it work better? We see the market timing to be right. The second one is how well-prepared are we on our deliberate effort of investment, deliberate effort of capability building, deliberate effort of the go-to-market function that we have built. That is where you saw that in growth office, again, which is a very deliberate investment from our side that we are focusing on reaching out to more customers. We are focusing on partners to create an ecosystem so that we can create larger deals and we can go to scenarios where value can be created, right? That is where the partnerships with Snowflake, Databricks, continuing the partnership with Informatica that we had, even from the MDM offering days are very, very important. Offers are evolving. I would say the two key offers are the two wins, like proof is in the pudding, and we see that AI-readable format is very different from human-readable formats. Data foundations are being reevaluated, re-looked at it. That is where the knowledge and the context layer was spoken. So that is a very key offering, which is AI-ready data foundation. The second one is applied AI, which is how do you apply AI to what you have so that you can generate value? Now, these two offerings, which are in two ends of the value chain, are becoming more and more important as we see the market evolving. Great. I appreciated the commentary in your prepared remarks around the growth office and the build-out there. What does success look like for the growth office, and how do you see that playing out longer term? I would divide it into two parts, right? One is, say, what are we focused on near term? I think near term are all early indicators that we are seeing, and we want to continue with it, which is how do we increase our qualified pipeline, how do we work with partners more and reach to more customers so that we can generate a pipeline which are generating value and build that stickiness. You will see that on how we move along in saying you will see this in bookings first before the revenue shows up. You will see a trend showing up in bookings on that. In a longer term, we want to focus very much on the large deal side. How do we create multi-year deals? How do we create larger deals? Because as I mentioned, customers are looking for full stack solves from data to AI, and that gives us a scenario of ensuring that how do we create a more compounding effect of growth office, all the levers coming together. The end goal would be not to just see it as a separate initiative. It should become part of our ways of working, repeatable. Every investment that we put in should lead to growth, as Kannan mentioned, that we are putting in a lot of deliberate investment, and that's how we see the growth office evolving. Great. Kannan, as we have about a minute or so left, but I did want to ask about the balance sheet, certainly, at the end of 2Q that remained quite strong. Maybe you could give us an update on your capital allocation prioritization at this point. Sure, Marc. I would frame our priorities into three key buckets. First is the organic investment. We spoke about growth office. We are into expanding our AI engineering capabilities, building proprietary tools and accelerators, and largely strengthening our go-to-market organization. That is where the majority of the near-term capital focus sits, and that investment activity will increase meaningfully in 2026. It's part of our EDGE. The second bucket is the share repurchase program. We did not repurchase any shares in the first half of this year. But there is an authorization from the board, and we'll be thoughtful on the timing of it. On the M&A, we have continuously started actively looking at the landscape out there. The honest answer is that our internal bar is pretty high because it's a make versus the build versus buy conversation that continuously happens. Any acquisition would need to be meaningful to accelerate our AI-first strategy. Our conviction right now is largely on the organic investment, but we will be very mindful about M&A, and we are thoughtful about how we go about investing our capital through that engine for growth as well. Excellent. Well, I thank you both for joining us here this morning as we get to the end of our time together today. Is leaving maybe for a few closing remarks this morning. Hey, sorry. Am I still audible? Sorry, there's a blip there. But, again, Marc, appreciate. Yeah, we can hear you. I really appreciate Sidoti to have given this opportunity. It's been wonderful being with all of you and kind of telling you our story of Mastech Digital and how excited we are to go through this journey, and Sidoti has been a critical component in that. Thanks to you, and thanks to all the listeners who have dialed in into our story. Pretty excited and thanks for your time. Okay. Thank you everyone for joining us this morning. Everybody have a wonderful and productive remainder of the day. Thank you, everyone. Thank you, everyone. Thank you.