Okay. Good morning, everyone. It is 9:15 A.M.. We are prepared to begin our next presentation with Mastech Digital, ticker is MHH. We thank you for joining us at the Sidoti conference this morning. Joining us tod ay is Chief Financial Officer, Kannan Sugantharaman, as well as Shipra Sharma, who is the Chief AI Officer. Kannan is the Chief Financial Officer. Again, we do thank you for joining us. Now, if you would like to submit a question for Q&A at the end of prepared remarks, certainly, you don't need to wait until the end of that.
You just click on the QA prompt at the bottom of your screen and feel free to submit questions and we'll leave a couple of minutes toward the end in order to get to those. With that, why don't we get started? For those who are new to the Mastech Digital story, can you begin with a brief profile of the company for those?
Certainly, Marc, thanks a lot for Sidoti to have given us this opportunity to be part of this event. Thanks for that. We do have a presentation and I have Joel Fernandes, who is our Head of Strategic Finance, joining us along with Shipra Sharma, who heads our AI. I wanted to quickly get the introductions done. Mastech Digital, and I will be crisp and spend the next 10 minutes kind of getting you up to speed on where Mastech Digital is today. Mastech Digital is a data and AI transformation firm. Our singular purpose is helping enterprises become AI ready, not just AI curious or not running pilots and experiments, but actually AI ready. We do that in two ways. First, high quality data and AI talent, which is the staffing piece of our business. We place specialized professionals inside enterprise technology teams.
That's roughly 70% of our revenues and is our stable cash generator foundation. The second is where we are investing hard and investing all our efforts today is in the data and AI services, AI ready infrastructure, advanced analytics, and agentic AI solutions. That makes up to about 30% of our revenue today and is our growth engine. The combination of these two businesses between the staffing talent piece and the data and AI piece, the combination is pretty deliberate. Our talent business, the staffing business, gives us embedded relationships into 300-plus enterprise customers, and that proximity creates the pipeline for our services team to go solve for the bigger problems, same client, but higher value. In a nutshell, Mastech Digital, 300 clients, 1,500 people, NYSE listed, six global offices, 10-plus platform partnerships, 100-plus proprietary accelerators.
Now, as I introduce, I also want to kind of take you briefly through how our business model works. Our business is a two-engine model. As I told you, that is the whole talent, which is the staffing piece of the business, is our foundation, stable, recurring, opens doors. Data and AI services is the growth engine. High margin, higher differentiation, deepens the relationship with our customers, and these two businesses kind of feed into each other. The depth is what is distinctive. 20-plus years of building trusted data foundations, Master Data Management, governance quality, observability. That's not something a new entrant can replicate easily. We bring in what we call a trifecta to every engagement. One, deep industry knowledge. Two, the talent to execute. Three, the platform capability to deliver at scale across multiple data platforms. That includes Snowflake, Databricks, GCP, Azure, AWS, and Informatica.
The services mix between these two businesses, be it the talent or the data and AI, is the investment thesis from Mastech. As data and AI services grow from the 30% towards a larger share, margins expand, and that's the path we are on. Now, here is what is driving that growth, the real problems that our clients are telling us and where they are stuck on, and which is the crux of how we are building this business. As we talk to our clients and as we engage with them, there are about five roadblocks that keeps coming in some combination of the lot, right? One, the pilot to production gap. Enterprises can run a proof of concept, they can run experiments and POCs, but they are unable to scale it because of the underlying infrastructure that is not enterprise ready. Two, the talent gap.
The AI and agentic engineering workforce simply does not exist. Three, the governance, security, and regulatory pressure, which I would say compliance requirements are creating real friction. Four, the total cost of ownership and value realization. Think about the token costs or the meteoric rise that is seen over the period and the ROI story is not that very clear yet. Case in point, we ourselves see this in our organization where we are building a customer zero environment, and when we are rolling out the AI tools to our own teams, the credits were exhausted in days. The appetite is real. The cost discipline isn't yet in place. five, adoption starts, right? Change management gets undermined and the whole human-machine interaction gaps don't surface till it's too late in the project.
These are not hypothetical, and I would want Shipra to chime in as to how we are building this journey, right? Every single one of those issues that I have just laid out has been coming time and again, and this is how we are kind of addressing the five as part of our enterprise AI journey. I bring in Shipra to kind of spend a few minutes on the next two slides where we talk about what our AI journey is and what we call our knowledge enterprise. Shipra.
Thank you. Thank you, Kannan. If you go to the next slide, I think one thing to appreciate and acknowledge is that the ecosystem that was so far built around and for humans, it now has to transition into an ecosystem that agents can work on, right. Many of the problems that we just mentioned in the previous slides is because everything so far has been built around human readability, whether it's our data, process, logic, all of it is assumed that a human can handle that level of ambiguity and take a decision. Cut to now, as of today, the data must move from being human readable to machine consumable, because agents don't understand the way the relational databases work, right. Systems must be ready to expose clean, callable interfaces, which is pretty much unlike before.
As more and more customers are on this journey, they've started to understand that the erstwhile data infrastructure now has to move into an agent infrastructure. The win is not in deploying the more agents, but the changing the entire stack that becomes more legible to agents. That's a little bit of an explanation into the why of the phenomena that we are looking at, right? If you go to the next slide, I think that explains our response, or Mastech's response to this thing. This is the core of our framework. We call it knowledge enterprise, or rather, Mastech enabling the knowledge enterprises of tomorrow, and it's built on four pillars. It starts with data.
Before any intelligence gets built, we go into our existing environment and we recover the data lineage, the governance, logic, semantics, which by the way, is already there in your systems, catalogs, business glossaries. There's a clean start, and that's available. Nothing to panic. On the top of that sits knowledge, the context layer, which is once the governed data and a trusted data is made available, we start activating the knowledge. Knowledge exists in relationships, in surfacing the correlations, and not necessarily the rows and the columns as we knew it in erstwhile world, right? Kicks in the orchestration, which is what we call the agents, which is where the AI now starts to do the work.
It has the data, it has the knowledge, or institutional knowledge, as we would put it starts the semantic searches, the conversational bots, automating certain tasks, making certain workflows happen. Now all of this is being done by agents, which are coordinating amongst themselves and across systems and is being used, and the policies are being enforced at every step of their way. They are being given an instruction on how to work, the AI is now being put into action at this layer. The final one is, which we call the discipline layer. It's not just enough to have all these things working and automation and workflows and agents, et cetera. It's equally important that every investment is actually has to be tracked for value. That what makes the program defensible for any enterprise.
By the way, the story is not any different for us, where, for example, even we have to justify the growing token cost against our AI investments, right? This is the new operating model. What you see on the slide is the new operating model, where humans and agents are working in the same ecosystem, where humans are providing the judgment while agents are executing with full contextual awareness as it is made available to them. I think I'll end with the next slide, which says very good, great, but why Mastech? If you go to the next slide, any consulting firm can show up with a framework like that on a slide. A smart consulting firm, let me qualify. What they cannot show up with is 20 plus years of actually living inside enterprise data environments, and that's our legacy.
The 20 years of experience in data management, data governance, stewardship, legacy-laden reality of how actually data works. That institutional knowledge is genuinely very hard to replicate, and it shapes every engagement that we do. That experience or that legacy alone is not the moat. It's just the starting point. What we have done is we have taken that experience and baked it into repeatable assets. The first one is, what I call the layer of pre-built ontology. Essentially a shared vocabulary across domains or across industries like retail, for energy, for travel. We are not inventing a semantic layer for every customer, right? For example, for one of the largest convenience retail stores in U.S., in North America, we are building ontology specific to their retail domain, and we have just started with a product catalog.
We'll extend it to stores, we'll extend it to customers, and as more and more use cases are solved, we will bring in merchandising, loyalty, et cetera. This will become the holy grail for any retail organization. Let me talk about how do we solve the pilot to production problem, right? Kannan talked about it. We are building something proprietary to address this directly. As the agent layer starts to get activated. We have developed something we call ADEPT, A-D-E-P-T, all caps. This is our proprietary asset. It works in the agent layer. It is our framework for embedding agents into client systems. The keyword here is into existing client systems. We are not going to ask customers to rip and replace their existing systems, and we are not going to be building custom AI from scratch.
ADEPT will take proven off-the-shelf models connected through standard interfaces like MCPs. It will put the agents deep into productions and make sure that they're working fine, they're working what they were supposed to do, they are being monitored, and all sorts of costs are even taken care of. Make no mistake, all of this has to be enabled through partnerships and a huge investment in our own internal workforce transformation and governance of it, which you see at the bottom is essentially the enablers for any system that is built around these four pillars of framework. Can we go to the next slide?
Thanks for that, Shipra. I'm sure you would see the new approach that we are taking to the market, especially in the data and AI space. Let me kind of sum it all up. I know at the top of the 15 minutes that I did have planned for. Let me put Mastech's case in one sentence. Pure play data and AI transformation firm, NYSE listed, a fortress of a balance sheet, accelerated bookings, as you can see in quarter one performance of 2026, and a strategy purpose-built for the AI first economy. With a proprietary framework and 20 plus years that Shipra spoke about in terms of enterprise data depth, that new entrants will not be able to replicate. We believe this transformation underway at Mastech Digital has not yet been priced in. I'll leave you with four things. One, capabilities.
100 plus proprietary accelerators, ADEPT, which is the proprietary platform that Shipra was talking about for agentic, live agentic deployments, and end-to-end platform engineering across every major stack, be it Databricks or Snowflake or GCP or Azure or Informatica. Two, clients. 300 plus clients accounts with the Fortune 500 and more. Q1 2026 new bookings in data and AI nearly doubled year-over-year, which is more the conviction that the market and the customers are giving us with respect to what we are bringing to bear in terms of our solution set. Three, our team. Seasoned executives averaging 20 plus years of experience in large format companies that includes Cognizant, Informatica, LTIMindtree, ManpowerGroup, and boutique players like Mu Sigma. Unusual depth of leadership for a country of our market cap. Four, financially.
Close to $35 million of cash in the balance sheet, zero debt, $5 million of buyback authorized, and +$20 million available in terms of term loans for acquisitive growth. What I can tell you is that the earnings expansion path is quite visible with the shift of services mix from this talent and staffing, which we are focusing on, into data and AI, which is what we are investing upon. That services mix pretty much calls for a new investment thesis at Mastech Digital. That's all we had for you today in terms of our prepared remarks out there. Marc, over to you.
Thank you very much. I wanted to start with, you announced the introduction of new segmentation, which was revealed when reporting 1Q results last week. Maybe you could discuss the thought process in the decision there and what you're looking to achieve?
Sure. No, certainly, Marc, right? We historically had two business lines, even in the past. It used to be called IT staffing services and data and analytics services, which were essentially offerings based on business lines. Starting 2026, we revisited our approach and moved to a customer-based classification to arrive at the two new reporting segments, which is Talent and Data and AI, as we just discussed. The rationale behind realigning these segments was fundamentally about how we believe these relationships are best served and grown in terms of account management. That decision to realign certain customer relationships into our Data and AI segment was driven by three clear objectives. Number one, it creates the opportunity for us to cross-sell a broader services portfolio in a more natural way into our existing client team.
Two, it allows us to deliver more integrated offerings, bringing together our data platform capabilities, our AI engineering expertise, and talent staffing in a cohesive manner. Number three, it positions us to deepen those relationships over time to engaging more directly with customers and client decision makers on strategic priorities. Taken together, these three broad, clear objectives reflect our belief that the most valuable client relationships are largely built on depth and breadth. That realignment in terms of account management is pretty much designing the way we are creating the new structure at this point in time. Marc, that's pretty much why we did what we did.
Okay, great. Starting with the talent side, in the quarter, average bill rate for 1Q was a company record high for the segment. Maybe you could share some of the drivers for those gains and how you view both future growth and visibility of rate increases?
Thanks for highlighting that, Marc. It is a result of, and we are very proud of that, and I want to be clear that that decision was pretty deliberate. The all-time high bill rate of close to $91, that's $180,000, $190,000/ annum, is the direct outcome of two pronged approach that we have been executing on this consistently over the last four quarters. First, we have been intentionally shifting our mix towards higher skill set, higher value roles, particularly in the data and AI discipline, where specialized talent commands premium rates. Second, that we have been deliberately exiting lower bill rate, lower margin positions that do not meet what we call the revenue quality threshold that we have, right? That has contributed to the headcount decline we discussed, but it has also structurally improved the economics of the business.
I would say on forward visibility, we don't provide guidance. As you know, we remain constructive out there, but we intend to continue our approach and monitor market dynamics largely closely as we move through the year. Marc, let me again tell you that what we did with the talent of the staffing business has largely been part of a strategic, deliberate move for us to move into better quality of revenue and better margins that way.
Okay, excellent. Now, last week announced was new booking wins. New bookings, I'm sorry, increased in the quarter to nearly double what was seen in the prior year period for data and AI segment. Maybe you could share some of the catalyst behind those gains and overall booking trends.
Yes, Marc. We have been very aggressively targeting these strategic deals. Given what we just spoke of in terms of the solution offerings that I explained and Shipra explained, right? Sharpening our go-to-market approach, investing in our solution engine, and being more deliberate about the types of engagement we pursue. I would say the results are starting to show. Two deals this quarter are worth highlighting. The first is a healthcare payer engagement. A top 10 payer in the country where we are building a next generation AI-ready data platform. That relationship actually started through Master Data Management that we did a while back and evolved into a broader data modernization engagement in the Microsoft platform, a direct result of our ability to cross-sell and deliver integrated offering. The second is the large MDM engagement with a major building materials company that recently went through a merger.
Post-merger, there was data complexity, and is one of, I would say, the more acute data challenges that an enterprise can face, especially post-merger. This is where our MDM expertise is what they were exactly needing. This is our core capability for us, and winning this deal largely reflects our continued strength in the master data management space. What you're seeing in the bookings number is really two things working together. One, we are doubling down on our core MDM capability, master data management capabilities, where we have proven track record. Two, we are expanding into more integrated cross-sell opportunities where we can deliver greater value to our clients. Both are showing results, and we really are intending to keep pressing on both fronts while focused be delivering on the whole data and AI narrative that we just explained as to what our moat is. Marc?
Excellent. Now since the November launch, EDGE, and for those who may not be familiar, that stands for Efficiencies Driving Growth and Expansion. That's clearly driven improvements. Can you discuss and share some of the current thoughts about the early execution of EDGE to drive those improvements and what we're likely to see in the months ahead?
Marc, EDGE was launched in Q3. Efficiencies Driving Q3 of 2025. Efficiencies Driving Growth and Expansion. It was a strategic transformation initiative that we launched, has been very successful for us. Continue to deliver quarter-over-quarter since the third quarter of last year. Our non-GAAP SG&A spend reduced by approximately, I would say, on a quarter-over-quarter basis, was about $2.1 million. Year-over-year, on an annualized basis, that's north of $8.5 million. That is purely by design, right? When we launched EDGE in Q3 of 2025, we were clear that the savings had to come ahead of our investments. That discipline has held, right? The $2.1 million reduction for the quarter one against quarter one of last year is a tangible proof of that commitment that we made to ourselves. What is equally important is what happens next, right?
EDGE stands for Efficiencies Driving Growth and Expansion. We got the efficiencies. We need the growth and expansion. We are now at a point where savings are being established and the reinvestment phase is beginning to pick up steam, right? Our intention is to reinvest substantial portion of those annualized savings back into our strategic priorities. We are not looking to bank these gains as margin improvements, and I'm being upfront about it. We are looking to deploy them as a fuel for our growth. 2026 becomes our year of execution, our year of investment, so to say. In terms of how we are prioritizing that investment, I would frame it in three broad areas. One. Our agentic AI engineering and modern data platform capabilities, this is where we are going to invest most disproportionately. We spoke about it, Shipra spoke about it.
Second, our go-to-market organization, strengthening our sales solutions, account management, new logo sales engine, that is already showing results in our booking momentum. Third is to invest in our people and leadership, bringing in domain expertise, industry orientation, which is verticalization, largely, that will allow us to compete and win larger and more strategic engagements with specific use cases that are vertically driven. That's how we are thinking about investments going forward, Marc.
Excellent. Shipra, first of all, congratulations on your new role as Chief AI Officer for Mastech. For those who have not had the opportunity, maybe you could take a minute to introduce yourself and share your initial thought. Oh, I think you're muted.
There you go. Sorry. Thank you for that. I'm Shipra Sharma. I've been at Mastech for a little over nine months now, and I'm currently the Head of AI Business Portfolio. Prior to that, I've spent eight years at Bristlecone. That's a niche supply chain specialist services firm. There we led or was part of the transformation where we transformed the company from a traditional SAP implementation to a supply chain digital services provider. Part of that, or rather part of my job, was to build risk and visibility solutions, products, and accelerators. Just for your information, it was during the COVID time when supply chain was a boardroom conversation and just as much AI is today.
Some similarities there. Before that, I've spent seven years at Mu Sigma. Mu Sigma was India's first analytics unicorn, if some of you may recall. That's where I learned the tricks of all data and analytics work and worked with a great deal of Fortune top 10, top 50 companies, Walmart, The Home Depot, United Airlines, like pretty much who's who. Learned a lot there and that's where I learned the tricks of the trade. That's a bit of my background.
Excellent. How would you say you view artificial intelligence as both the demand driver as well as an internal efficiency benefit too?
Yeah. That's a good one. In my view, the efficiency as well as the demand driver, they both have to happen simultaneously. They are both happening simultaneously. I don't think anybody has a choice in that matter. Let me explain a little bit. If you think about it, there's a strategic tension between the two. If AI makes our delivery teams dramatically more efficient, then I can deliver the same scope with fewer people. Which means that my traditional billing model, which is usually FTE based in IT services firm, it means that I make less revenue for the same outcome. Literally this morning, I was working on a case where we would actually have to cannibalize our managed services revenue because we are proposing that we'll build a tool, an AI-powered tool, of course, that will cut down the team by X%.
I think there isn't much of a choice. I think a little bit of it is a forcing function of the fact that the technology is there. The thing that we can do is that we can harness the efficiencies and redeploy it into the investments or into newer business models or the top-line growth. Both will happen together. The efficiency and the demand will play a dance, in my view, for the foreseeable future to come.
When it comes to data and AI, could you discuss the industry solutions and accelerators approach as well as, are there any customer industry verticals that stand out when it comes to our catalyst and demand growth?
Right. I think Kannan mentioned this. I think from the very beginning, we have thought through the path on how we want to differentiate, what is it that we carry and what is that we are going to build as a legacy. Two, three things. Our lineage or our experience has been in data, and I think we carry that with a great deal of pride and a lot of experience and a lot of work that has been done in that. That comes first from what we carry. We have invested, or we are investing a lot in building the agentic frameworks, platform accelerators, which are mostly what I call the technology accelerators because they are accelerating the time to value.
On the top of it, what we are building is almost think about the Lego-type solutions, small building blocks that are very tuned to industry or vertical use cases. At the intersection of these three, which is the data, the technology accelerators, and a deep domain knowledge of certain industry vertical use case, I think that gives us a distinct advantage in terms of building industry use cases very specific to the customers that we serve, to the industries that we serve, if that makes sense.
Excellent. We're getting close to the end of our time together, but I did want to sneak in one more on the balance sheet and the strength of the balance sheet as mentioned in prepared remarks. How should we view thoughts on capital allocation prioritization at this point?
I go back to the statement that I made, which is we have a fortress of a balance sheet. It is indeed strong. We have $35 million in cash roughly, and $21 million that's available as a line of credit for us. I would frame our priorities in three buckets. First and the most immediate is the investments that we are making in the capabilities that we are building right now, our agentic AI engineering, proprietary tools, and accelerators, and strengthening, and our go-to market. That is where we believe majority of our near-term focus sits, and we expect the investment activity to be meaningful through the remainder of 2026. The second bucket is going to be in terms of our M&A. We are always looking at the landscape.
The honest answer is that our bar is pretty high, but we are very clear with respect to what we want out there. It's going to be an intersection of three things coming together, a vertical focus, a data and AI focus, our ability to kind of get into some of these niche partnerships in data platforms like Databricks and Snowflake. That's going to be the intersection that we are going to look at from an M&A standpoint. That's going to be a critical element out there, and there is always the element of share repurchase. Last quarter we did not, but there are funds that are being earmarked for bulk purchases, so to say. The way we think about capital allocation is largely simple, right?
That every dollar we deploy should accelerate our ability to win in the market, in where we have chosen in order to compete. Organic investments gives us the most control over outcome right now, and that's where our conviction is the highest, so that's what we are focusing on at this point in time. Our immediate, I would say, priorities would include getting into inorganic activity and really getting that 1 asset that will help us in furthering the capabilities that we just spoke of.
Great. I do want to thank Mastech for joining us today, as well as all of our participants for joining us this morning. Everybody have a wonderful and productive remainder of the day. Do you have any closing comments that you'd like to share with everyone?
No, I appreciate it. The one great question that I keep, honestly, is a misconception that we want to really break, Marc, and you've seen through those, right? Honestly is the biggest conception is that Mastech is simply a staffing company. That perception is something we are actively working to change. Forums like these with Sidoti gives us a very good opportunity, an invaluable opportunity to tell our story directly. Thanks, Marc, to you and Sidoti. Over the past year, we have been very consistent and deliberate in our messaging. We want to be an AI-first company. We have now backed that up with action. The resegmentation we announced this quarter in Q1 is not just a messaging exercise, it's a structural commitment that actually reflects what we are going to do, where we are going to do it, and how do we intend to compete.
We also have a new management team that we believe the market has not fully appreciated yet. These are industry veterans across our lines of businesses who bring in deep domain and real operating credibility. We have technical leaders like Shipra, who bring in genuine AI engineering depth to our narrative. This is not a company talking about AI from outside. We are building it as a customer zero environment inside the company. We are genuinely hopeful about what is ahead of us. We believe the investments we are making, the capabilities we are building, the reorganization we have executed really position us for a different kind of growth than what the market has historically associated Mastech with or the kind of growth in the business segments.
We feel the market has not fully priced that in, and we intend to keep showing up, keep delivering, and let I would say, let the results make that case over a period of time. That's my closing commentary, Marc.
Excellent. Well, thank you so much for joining us this morning, and everybody have a wonderful and productive remainder. Thank you very much.
Thank you.
Thank you.