Mastech Digital, Inc. Sidoti Micro-Cap Investor Conference

Thank you, Mark, 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 one-third of our business today and our growth engine. 300-plus enterprise customers, 1,500-plus people, 10-plus 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-plus 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, Amazon Web Services, 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 2, 3 slides, is designed specifically to address those 5 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 3 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 4 pillars, what we call the knowledge enterprise. 4 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-plus 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.