Mastech (MHH) Q3 2026 Earnings Call Transcript

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DATE

Fri, Oct 30, 2026

CALL PARTICIPANTS

• Investor Relations - Jennifer Lacey
• Chief Executive Officer - Nirav Patel
• Chief Financial and Operations Officer - Kannan Sugantharaman

TAKEAWAYS

• Consolidated Revenue -- $42 million, representing a 13.4% decrease year over year driven by lower billable consultant counts in the Talent segment.
• Data and AI Revenue -- $14.1 million, representing sequential growth of 4.7% reflecting the second consecutive quarter of expansion for this segment.
• Talent Revenue -- $27.9 million, a 16.4% decrease compared to the prior year period due to insourcing activities at a top 10 client.
• Data and AI Bookings -- $10.8 million on a total contract value basis, up 29% from $8.4 million in the third quarter of 2025.
• Average Bill Rate -- $92.37, reaching an all-time high for the company and up from $89.65 a year ago due to a focus on high-margin revenue quality.
• Gross Margin -- 29.0%, up from 28.3% in the prior year period reflecting a shift toward higher-value digital transformation services.
• Data and AI Gross Margin -- 40.5%, increasing by 50 basis points year over year due to disciplined pricing and higher-value project wins.
• Talent Gross Margin -- 23.1%, representing a slight increase from 23.0% in the prior year period despite lower overall segment revenue.
• Non-GAAP Net Income -- $1.2 million, or $0.10 per diluted share, compared to $3.5 million, or $0.29 per diluted share, in the prior year period.
• GAAP Net Income -- $7,000, or $0.00 per diluted share, reflecting impact from stock-based compensation and amortization of intangible assets.
• Billable Consultant Base -- Declined by 154 consultants since the third quarter of 2025, a 21% reduction resulting from the exit of non-strategic staffing positions.
• Cash and Liquidity -- $37.9 million in cash on hand with $18.3 million available under the credit facility and no bank debt outstanding.
• Data and AI Headcount -- 580 employees, down 14% on a sequential basis as the segment optimized its resource delivery model.
• Day Sales Outstanding -- 56 days, remaining consistent with the prior year period and showing improvement over the last few quarters.
• Share Repurchase Authorization -- $5 million remains available as the company did not repurchase any common stock during the third quarter.

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RISKS

• Sugantharaman stated, "As we enter the fourth quarter, we expect the seasonal effects that typically affect our industry, including holiday related slowdowns and year-end contract completion," noting potential impact on the upcoming reporting period.
• Sugantharaman noted that the decline in the billable consultant base was driven by "insourcing activity at one of our top 10 clients and a deliberate exit from certain low margin non-strategic staffing positions."

SUMMARY

Mastech Digital, Inc. (NYSEMKT:MHH) reported a 1.3% sequential increase in consolidated revenue, supported by momentum in the Data and AI segment which grew 4.7% sequentially. Management reported that the company has transitioned from a reset phase in 2025 to an investment phase in 2026, focusing on its new Knowledge-first Enterprise branding and the reinvestment of efficiencies gained through its EDGE program. While the Talent segment faced headwinds from client insourcing and a strategic exit from low-margin contracts, the company maintained record-high bill rates and a solid liquidity position with zero bank debt.

• Patel stated, "Our differentiator is the ability to turn a client's own data and knowledge into practical, applicable, and scalable AI solutions that deliver outcomes."
• Management reported securing its largest AI deal to date, an agentic AI audit platform built on AWS that automates vendor and invoice audit processes.
• The company expanded its Microsoft partnership, achieving solution partner status for cloud and AI, which management stated strengthens its delivery capabilities for modern data foundations.
• Patel reported the alignment of service offerings into three core areas: applied AI, modern data, and AI governance, trust, and safety.
• Sugantharaman stated, "The efficiencies we have created are now being reinvested in the capabilities our clients need to turn their data into knowledge and their knowledge into decisions and outcomes."
• Management indicated that the impact of insourcing at a major client is largely behind the company, though it lowered the baseline for the Talent segment going forward.

INDUSTRY GLOSSARY

• Agentic AI: AI systems designed to use reasoning and tools to autonomously complete complex, multi-step tasks or business processes.
• DSO (Days Sales Outstanding): A measure of the average number of days it takes a company to collect payment after a sale has been made.
• EDGE (Efficiencies Driving Growth and Expansion): A Mastech-specific strategic initiative focused on creating operational efficiencies to fund growth investments.
• MDM (Master Data Management): A technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, and accountability of an enterprise's official shared data assets.
• TCV (Total Contract Value): The total value of a contract over its duration, including one-time charges and recurring revenue.

Full Conference Call Transcript

Operator: Good day and thank you for standing by. Welcome to Mastech's Third Quarter 2026 Earnings Conference Call. [Operator Instructions] Please be advised, today's conference is being recorded. I would now like to turn the conference over to your speaker today. Jenna Lacey, please go ahead.

Jennifer Lacey: Thank you, Operator, and welcome to Mastech's Third Quarter 2026 Conference Call. If you have not yet received a copy of our earnings announcement, it can be obtained from the investors section on our website at www.mastech.com. With me on the call today are Nirav Patel, Mastech's Chief Executive Officer, and Kannan Sugantharaman, our Chief Financial and Operations Officer. I would like to remind everyone that statements made during this call that are not historical facts are forward-looking statements. These forward-looking statements include our financial growth and liquidity projections, as well as statements about our plans, strategies, intentions, and beliefs concerning the business, cash flows, costs, and the markets in which we operate.

Without limiting the foregoing, the words believes, anticipates, plans, expects, and similar expressions are intended to identify certain forward-looking statements. These statements are based on information currently available to us, and we assume no obligation to update these statements as circumstances change. There are risks and uncertainties that could cause actual events to differ materially from these forward-looking statements, including those listed in the company's 2025 Annual Report on Form 10-K, filed with the Securities and Exchange Commission, and available on its website at www.sec.gov. Additionally, management has elected to provide certain non-GAAP financial measures to supplement our financial results presented on a GAAP basis.

Specifically, we will provide non-GAAP net income and non-GAAP diluted earnings per share data, which we believe will provide greater transparency with respect to the key metrics used by management in operating the business. Reconciliations of these non-GAAP financial measures to their comparable GAAP measures are included in our earnings announcement, which can be obtained from our website at www.mastech.com. As a reminder, we will not be providing guidance during this call, nor will we provide guidance in any subsequent one-on-one meetings or calls. I will now turn the call over to Nirav for his comments.

Nirav Patel: Thanks, Jenna. Good morning, everyone. We appreciate you joining us as we review our third quarter 2026 results. Before Kannan walks through the segment detail, I want to share some updates on our vision for Mastech and how we are investing towards building an indispensable moat that positions us well to deliver on our objectives. This quarter we announced our vision of the Knowledge-first Enterprise and introduced a new positioning for the Mastech brand. We believe our differentiator is the ability to turn a client's own data and knowledge into practical, applicable, and scalable AI solutions that deliver outcomes.

Early response to our launch from our strategic partners and customers has been very encouraging, and we hope to amplify the brand further over the coming year. We believe the moat for an enterprise is its knowledge and people, not an AI model. Becoming a Knowledge-first Enterprise means bringing data, business context, and human judgment together so people and AI can work with the same understanding across the business. Today, knowledge is scattered across an enterprise's people, processes, code, tools, systems, and platforms. It includes tacit knowledge around how people exercise judgment when they make an exception and why a decision was made. Harnessing that knowledge requires the transformation in the operating model.

A strong data foundation, knowledge infrastructure, the right applied AI solutions, and AI-first talent, and adoption working together to produce value and achieve outcomes. To deliver on this vision, we are aligning our offerings, capabilities, and assets around three core offerings: applied AI, modern data, and AI Governance, trust, and safety. Applied AI turns enterprise knowledge into solutions for targeted business problems. In this offering, we combine agentic technology with our own engineering talent to move those solutions into production so they scale and are adopted rather than remaining pilots. They build across the leading frontier models our clients already use, including those available on Azure, Google Cloud, and AWS.

Modern data builds the data foundation for insights, analytics, and AI. In this offering, we modernize the data and make it usable at scale, supported by a governance layer that keeps it trusted and secure. We have built this offering anchored by our partnerships in the likes of Snowflake, Azure, and Databricks. Our AI governance, trust, and safety helps enterprises lay an AI foundation to scale AI responsibly. This offering focuses on core building blocks of security guardrails, governed data access, an enriched knowledge base, dynamic model routing, evals, and observability that move AI from pilots to enterprise-wide scale. Together, we believe these offerings connect a trusted data foundation and enterprise knowledge with practical applied AI solutions.

Modern data makes data usable at scale. AI governance, trust, and safety makes the data trustworthy, and applied AI turns the foundation into business outcomes. We believe these offerings and capabilities are fundamental for AI transformation. And we plan to invest over the coming years to pivot and strengthen our play as we help companies become knowledge-first enterprises. Our partner ecosystem supports this direction by strengthening our delivery capabilities with the technology platforms our clients already use. We have expanded our Microsoft competencies to support applied AI delivery alongside expertise in Snowflake, Azure, and Databricks to build the modern data foundation.

We expect to continue investing in the talent, skills, and capabilities needed to turn competencies into solutions our customers can adopt at scale. Turning now to our business segment performance during the third quarter. Our talent segment continues to reflect the revenue quality discipline we have described on prior earnings calls. The average bill rate reached another Mastech record high of $92.37 this quarter, up from $89.65 a year ago, even as we continue to work through in-sourcing activities from one of our top 10 clients and our own deliberate exit from certain low-margin non-strategic positions.

We are very pleased to share that our data and AI segment delivered a second consecutive quarter of sequential revenue growth, up 4.7% from the second quarter, with bookings of $10.8 million, up 29% from $8.4 million in the same quarter last year. One engagement from the quarter worth highlighting is that we secured our largest AI deal, expanding our work with an existing client through a new initiative. This is our applied AI offering in practice, modernizing a highly manual vendor and invoice audit process through an agentic AI audit platform built on AWS.

Leveraging AI to extract and reconcile data, human-in-the-loop controls, and a reusable data foundation, our solution is structured to deliver a secure and scalable platform that will streamline audits, support compliance, and help recover overpayments. The client expects significant savings from the first run alone. We believe this is an example of how we bring enterprise knowledge and applied AI together to deliver a key business outcome. In summary and in our view, 2025 was a year of reset. We evaluated our strengths as a company and began unlocking efficiencies through EDGE, while 2026 has been the year of putting those efficiencies to work through investments and building true differentiation.

We believe our brand positioning, offerings, strategic relationships and investments are all aligned towards the vision of a Knowledge-first Enterprise. We remain focused on building differentiation through execution. I expect to share more details as we build on the momentum during the quarters ahead. With that, I will turn it over to Kannan, who will walk you through the financials.

Kannan Sugantharaman: Thanks, Nirav. Good morning, everyone. As Nirav mentioned, data and AI grew sequentially again this quarter. That's two quarters in a row. And the first time we have done that since 2024. I want to thank our team for the work behind it and our clients for their continued support. We believe this growth shows that EDGE is working. As a reminder, we launched EDGE, Efficiencies Driving Growth and Expansion, in Q3 2025 with two phases: an efficiency phase and an investment phase. Last quarter, we said that the reinvestment of those efficiencies into our leadership and talent, our competencies, and our market growth initiatives had started to accelerate.

We believe that the growth we are reporting in data and AI today reflects that investment. It also connects directly to what Nirav shared about our new brand and our vision of the Knowledge-first Enterprise. We believe EDGE is the foundation that makes that vision achievable. The efficiencies we have created are now being reinvested in the capabilities our clients need to turn their data into knowledge and their knowledge into decisions and outcomes. We plan to continue to invest significantly in talent, in building competencies, and then expanding our data and AI business. And we believe EDGE positions us well to build differentiated growth around the Knowledge-first Enterprise. I will now discuss our third quarter financial results.

During the third quarter, we delivered consolidated revenue of $42 million, a 13.4% decrease year-over-year compared to the prior year period. Our Talent segment delivered revenue of $27.9 million, 16.4% lower than the prior year period, though our focus on revenue quality continues. Our bill rates once again reached an all-time high for Mastech at $92.37, up from $89.65 a year ago. Our billable consultant base declined by 154 consultants since the third quarter of 2025, a 21% reduction. This reflects the same two factors we have discussed over the past few quarters: insourcing activity at one of our top 10 clients and a deliberate exit from certain low margin non-strategic staffing positions.

We believe the impact of these factors is now largely behind us, though they do lower the baseline going into future quarters. Separately, as we enter the fourth quarter, we expect the seasonal effects that typically affect our industry, including holiday related slowdowns and year-end contract completion. Our data and AI segment reported revenue of $14.1 million, a decrease of 6.8% compared to prior year period, but a growth of 4.7% sequentially. On the booking side, third quarter bookings totaled $10.8 million on a total contract value or TCV basis, compared to bookings of $8.4 million TCV in Q3 in the prior year period. The third quarter is typically a seasonally slower booking period for us.

So we view this year-over-year growth as an encouraging sign. Gross profit of $12.2 million was a decrease of 10.5% compared to the prior year period, but in gross margin terms, we have seen an improvement of 50 basis points from 40% to 40.5%. GAAP net income for three months ended September 30 was $0.00 per diluted share compared to GAAP net income of $0.9 million, or $0.08 per diluted share in the prior year. Non-GAAP net income for the three months ended September 30 was $1.2 million, or $0.10 per diluted share compared to $3.5 million, or $0.29 per diluted share in the prior year period.

GAAP net income for the nine months ended 30 September was $0.2 million, or $0.01 per diluted share compared to a GAAP net loss of $0.4 million, or minus $0.03 per diluted share in the prior year period. Non-GAAP net income for the nine months ended September 30 was $3.4 million, or $0.29 per diluted share compared to $6.1 million, or $0.50 per diluted share in the prior year period. During the third quarter of 2026, our liquidity and overall financial position remained solid. On September 30, 2026, we had $37.9 million in cash on hand, no bank debt outstanding and cash availability of $18.3 million under our revolving credit facility.

Our day sales outstanding on September 30, 2026 totaled 56 days, which is in line with our DSO measurement a year ago and an improvement over the last few quarters. During the third quarter of 2026, we did not repurchase any shares of Mastech common stock. And as a result, as of September 30th, 2026, the entire $5 million remains available under the share repurchase program. Operator, this concludes our prepared remarks. We will now open the line for questions.

Operator: [Operator Instructions] Our first question comes from Marc Riddick with Sidoti.

Marc Riddick: I wanted to start with some of the commentary around the new -- the brand and the commentary in the press release regarding the Knowledge-first Enterprise. Maybe you could talk a little bit about how we should view that and what that means for Mastech, how that impacts your go-to-market strategy and some of the things that you're trying to accomplish there? And then I have a follow-up after that.

Nirav Patel: Thanks, Marc. This is Nirav. I'll take that question. So thanks for the question. Let me start with the context because it shapes everything we are doing. The pace of AI innovation from the Frontier Labs is remarkable, but as these models become widely available, the model itself is no longer what sets an enterprise apart. A foundation model doesn't know what your people know. It doesn't know your processes, your customers, or the judgment your teams have built up over the decades. That knowledge is, to us, is the real moat, and it belongs to the enterprise. The challenge is that this knowledge is really, really scattered. Some of it sits in machines, tools, platforms, systems of record.

But much of it actually sits with people in processes, in tacit know-how and in their judgments. A Knowledge-first Enterprise brings all of that together, connecting data, business context, and human judgment, so people and AI work from the same understanding across the business. Now doing that isn't a single project. It's change in the operating model. Think of it as a well-oiled machine, you know, you need the right data foundation. You build the knowledge infrastructure on top of it, and then you apply the right AI model, and you pair all of that with AI first talent and real adoptions, right? Because that's where the real value is ultimately created.

So that's what it means for Mastech going forward. You know, really as we support our clients and partners, our three offerings really map directly to that machine in terms of how we talked about it. So how we go to the market and the engagement we pursue, we will lead with outcomes, not technology for its own sake. We'll help clients codify their knowledge. So it really, really delivers results that can measure. So sorry for a little bit of a longish answer, Marc, but I think it's important for us to really give and provide that context of the Knowledge first.

Marc Riddick: Okay. No, I do appreciate that. And then maybe we talk a little bit about the pacing of what you're seeing, particularly with the sequential growth that we're seeing in the data AI segment, some of the drivers that you've seen. And maybe you certainly you have a customer base that's fairly diverse. I was wondering if you're seeing any differentiation in any particular key client verticals or anything that we should -- that stand out currently.

Kannan Sugantharaman: Yes. I think...

Nirav Patel: Go ahead, go ahead, Kannan.

Kannan Sugantharaman: Go ahead, Nirav, please.

Nirav Patel: No, I was just going to take this in two parts. I think you asked about the pacing of the whole brand and how this is really reflected and maybe I can have Kannan chime in on the deal win and so forth. So, but let me take a first part on the deal pacing, right? See, to us, the offerings that we talked about today are very fundamental in terms of how, you know, this will help enterprises become Knowledge first.

And our objective and vision that we are laying down now is very clearly that we want to go towards a play where these three core offerings, one which I talked about around modern data, which really builds the foundation. Then you talk about the AI governance, trust and safety that lays the foundation to scale AI responsibly because it's important that everything that you build needs to be production ready and needs to scale responsibly. And finally, using our applied AI offerings to convert that strong foundation into business outcomes. So this is how we sort of thought about the three broad core offerings.

And for us, the pacing of this, if I look at the client's prioritization, they disproportionately are spending today in areas around data modernization and foundation, I call it. Because before the agentic world to scale up, I think you need the level of hardness. You need a level of foundation that is stable so that they actually get a more predictable growth with agents beyond that. So I think it's interesting to see that from a client standpoint, we see the conversations really begin with applied AI conversations, but they quickly turn into getting their data ready for AI and then laying a strong AI foundation to be able to scale them better.

So I don't know, Kannan, if you want to just chime in with your comments on the deal win.

Kannan Sugantharaman: Sure, Nirav and Marc, I mean, this is a deal which Nirav spoke about, we are really proud of this one. It's our largest AI deal to date. And it's one of our existing relationships and which expands that relationship into a very new initiative. The problem we are solving is a common one, right? So their vendor and invoice audit process was highly manual and depended heavily on auditors working through large volumes of documents and transactions. It was slow, hard to scale, and it meant overpayments that went undetected. That was the business problem. We are building an agentic AI audit platform on AWS. It has three elements.

First, the AI that extracts and reconciles the data across invoices and vendor records. Second, human in the loop controls. So the auditor's judgment stays at the center of the process. And third is the usable data foundation that a client can build on beyond its first use case. So the result, I would say, is a scalable platform designed to streamline audits, support compliance, and help with the overpayment and the recovery of it. And the client expects significant savings from the first run alone. I would say this is a very good example of what Nirav spoke about, which is the Knowledge-first Enterprise in practice.

We are capturing the knowledge, the judgment experienced by the auditors and we're then pairing it up with applied AI, which takes a process from a manual effort to a very measurable business outcome because it is built on reusable foundation we see this platform that can grow with client over time and not just a one-off project for our clients. So Marc that's how I reflect on that new opportunity there.

Marc Riddick: Okay, great. And the last thing you made mention of as we -- on the -- as far as where we are with EDGE and the investments that you're looking to make going forward and adding in Talent. I think the headcount in Data and AI segment was down a little bit sequentially, maybe you could talk a little bit about the pacing of what we might see there as far as the investments that you see going forward? And do you anticipate gains in the headcount in the near future? Or is that sort of a level setting -- do you anticipate a level setting of that? Or are we moving heads from a different place?

Or are you anticipating headcount gains going forward from there?

Kannan Sugantharaman: Right. So, let me address this in two parts. One you spoke about EDGE. Let me address EDGE first. And then Nirav had commented about it, right? 2025 was our reset year, 2026 has been the year of putting those efficiencies to work through the investments and building the real differentiation and the growth we are seeing in data and AI reflects that and it reinforces our conviction of what we are doing. So that's on the EDGE front, right? So we are making these investments with a multi-year view, Marc. We have spoken about this in the past.

We believe this is the right time to build a capability ahead of demand and we would encourage the investors to see them in that light. But as you would know, we do have $38 million roughly of cash and no debt. It gives us the flexibility to do while staying disciplined. Let me also talk about the talent part of it, right? So you're right our headcount for September was down to 580, which was 14% lower on a quarter-on-quarter basis, but actually 154 lower year-on-year. The year-on-year decline in talent reflects, as we have spoken, two factors we have discussed over the past few quarters. The first is the insourcing activity at one of our top clients.

And the second is the deliberate exit from certain low margin, non-strategic staffing positions, which also reflects how good our bill rates are at this point in time. We believe the impact of those factors are largely behind us. But Marc, you would know, that as we enter the fourth quarter, we do expect seasonal effects typical to the talent industry, including holiday related slowdowns and year-end completions, contract completions. But beyond that, we don't necessarily provide guidance, but we are monitoring the market closely, staying disciplined on revenue quality and positioning ourselves to take advantage of opportunities as they arise.

Operator: Our next question comes from Lisa Thompson with Zacks Investment Research.

Lisa Thompson: I'd like you to expand a little bit things you've been talking about. First off, those three new offerings you talked about, is these -- are these new capabilities? Or are these currently what you're doing and you've just kind of packaged them into distinctive segments?

Nirav Patel: Lisa, I'll take that question. Good morning. So it's a combination of those two. And let me explain what portions of our core offerings and how we have really, modeled our core existing work around, particularly around MDM as well as customer experience that we have been actually pretty good at in the past. How did we fold that into the right place. If you talk about for the three offerings, our applied AI offerings, right, it really turns what I call enterprise knowledge into solutions, business -- specific business problems, right? And we combine agentic technology with our own engineering talent to get those solutions into production. That is a completely new offering.

Like in a sense, like we never did that, you know, over the last few years. I think if you compare us from a couple of years before to now, right? So to me, all things applied AI are absolutely new, and some of our deal wins are very, very encouraging for us that these new capabilities are finding relevancy with our clients and are now starting to get to a certain level of scale, which gives us a feeling that clients are ready and that they want to really accelerate as much as possible with the applied AI offering that we have.

The modern data, and we call the modern data because we have had, like I said, historically we have really been a very strong MDM play with IBM as well as some of the other partners like Informatica, Reltio, and so forth, right? That area of our core work is now folded under what we call it as the governance, trust, and safety layer. And so essentially that's what the clients are like looking at it but I think it is completely AI-fying that core legacy offering. So I think it's getting rechristened. I think so it fits very well for us into that layer.

But the client clearly expects us to really completely modernize that governance, trust, and safety layer. So for us, you have newer elements of harness, you have newer elements of model routing, evals, and everything that comes along with AI is very nicely folding for us in that. Modern data is something that we have been more recently building upon, and I think that's also one of our newer offerings, where we are engaging more strongly with some of our data platform partners and more modern data platforms to really say that, hey, is that something that we can really go take across?

And I think our Microsoft certification and the expansion that we talked about in our remarks is really a true reflection for us in that regard because as our partners like Microsoft, where we've now become the solution partner for their cloud and AI status, really strengthens our positioning to really accelerate modern data capabilities. So that's also a relatively new capability for us. So these are all new capabilities. That's clearly where we are diverting most of our EDGE unlock or savings to. I mean, I think that's how we have really designed the model but I think the early signs are really, really encouraging that reflects in our bookings pick up as well as in our deal wins.

Lisa Thompson: Okay, interesting. So I was -- when hearing about these, it occurs to me that maybe these aren't really just projects. Is there a possibility of having recurring revenues from some of these abilities?

Nirav Patel: So they do, right? Because all things AI are pretty sticky in nature, right, Lisa. So when you go into -- and we spoke about a deal win with agentic commerce last time. And that's a little bit of a combination of our AI Foundation plus Applied AI opportunity. When customers start to really see success in the early cases of adoption and they see real outcomes, and I think clearly customers are asking us for outcomes and we are pushing for outcomes. The traditional model of IT services was extremely input cost driven. And we are now going towards a business outcome sort of play.

And as customers start to do more and more of this, they just want more of it. I mean, I think these relationships expand further when clients start to add it. So there is a level of platform build that comes along with it. And there is a level of stickiness that allows clients to really lean on the same AI partners that have proven outcome capabilities in the past. So we do believe there is a recurring nature of our business which will help and should help us strengthen and scale some of our clients that we began working with this new offering on to large scale relationships.

Lisa Thompson: Okay. Are you doing any work as far as trying to optimize customers' use of LLMs? It seems like there's a lot of talk between paying up for the leading EDGE ones and then just using open source for simpler tasks. Is that something at all you're involved in?

Nirav Patel: So I can't specifically talk about details of the projects, but let me give you a little color, Lisa. See, the AI governance layer, efficiency layer, the foundation layer, is bringing in what I call optimal use of AI. An optimal use of AI is now becoming extremely critical in all of our client engagements. I mean, the board is no longer asking the CEOs and the CTOs of our customers that show me AI and they're asking the question, show me AI at what cost? As soon as that question comes in that way, the focus is really to say that, you know, is it efficient?

I mean, we love the magic, but is the magic efficient enough for what it's costing us? So that is leading to extremely, what I call, efficient AI deployment conversations. Clients are clearly, asking us and taking our help and saying, hey, how can they efficiently reduce cost? But I think there are two groups of clients that you generally face. There are some AI clients who actually feel the pressures of AI Maxxing. But there are a lot of clients that don't see AI Maxxing issues, but they really want more ROI to be better on the AI investments.

So it's not like that they're running completely short of money to be able to deploy on AI, but the big question mark is if you're producing something in AI, what's the price am I paying to produce the same outcome? And does the ROI justify that business case for me taking that AI project into production?

Lisa Thompson: Okay, that's good to hear with some of that going on. And finally, I'd just like you to talk about that project that you were talking about, about the invoicing and billing, the new big one. Is that something that you can take to any industry and any company that just pays bills? And would that be a project you would just move from company to company? Or how would that work?

Kannan Sugantharaman: So let me take that, Lisa. And this is something that I did briefly cover. It is -- as I said, the problem we are solving is a pretty common one, right? And this is where you have -- the vendor and invoice audit process, which was hugely manual and dependent on the auditors working through large volumes of transactions, and it was slowing them and it was meaning an overpayment. What we are building is an agentic audit platform, which is repeatable from a solution standpoint, but it largely looked at extracting and reconciling the data.

We talk about human-in-the-loop controls, so auditors' judgments stay in the process, and that's where the reusable data foundation that the client can build beyond this first case. But to your question, we don't see this as one-off. Every new AI engagement and the solution we build for it adds to a repeatable library of agentic skills and tools. The solutions span different industries and drive different outcomes, but they can draw on the same underlying AI capability, which is data extraction, knowledge layers, agent orchestration, governance, and human-in-the-loop controls. So we believe that this is, that is, that it will create a compounding effect over time.

Each engagement makes the next one faster and deliver more value to our clients and we are pretty excited to see how it scales.

Operator: And I'm not showing any further questions this time. I'd like to turn the call back to Nirav for any closing remarks.

Nirav Patel: Thank you, Operator. If there are no further questions, I'd like to thank you for joining our call today. We look forward to sharing our fourth quarter and full year 2026 results with you in February.

Operator: Thank you, ladies and gentlemen. This concludes today's presentation. We thank you for your participation. You may now disconnect and have a wonderful day.

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