Q3 2026 Mastech Digital Inc Earnings Call

October 30, 2026

Operator: Good day, and thank you for standing by. Welcome to Mastech's Q3 2026 earnings conference call. At this time, all participants are in a listen-only mode. After the speaker's presentation, there will be a question-and-answer session. To ask a question during the session, you need to press star one one on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press star one one again. 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.

Speaker #1: After the speaker's presentation, there'll be a question-and-answer session to ask a question during the session you need to press star 11 on your telephone.

Speaker #1: You will then hear an automated message advising your hand is raised to withdraw your question. Please press star 11 again. Please be advised, today's conference is being recorded.

Speaker #1: I would now like to turn the conference over to your speaker today, Jennifer Lacey. Please go ahead.

Jenna Lacey: Thank you, operator, and welcome to Mastech's Q3 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.mastechdigital.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.

Speaker #2: 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.

Speaker #2: 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.

Speaker #2: 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.

Speaker #2: Without limiting the foregoing, the words "beliefs", "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.

Jenna Lacey: 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.mastechdigital.com.

Speaker #2: 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.

Speaker #2: 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 and operating the business.

Speaker #2: 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.

Jenna Lacey: 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.

Speaker #2: 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 Q3 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.

Speaker #3: 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.

Speaker #3: 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.

Speaker #3: 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.

Speaker #3: 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.

Speaker #3: 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.

Nirav Patel: 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 a 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.

Speaker #3: 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.

Speaker #3: Harnessing that knowledge requires a 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.

Speaker #3: To deliver on this vision, we are aligning our offering's capabilities and assets around three core offerings. Applied AI, modern data, and AI governance trust and safety.

Speaker #3: 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.

Nirav Patel: 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. We 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.

Speaker #3: We 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.

Speaker #3: 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.

Speaker #3: 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, and enriched knowledge base, dynamic model routing, evals, and observability that move AI from pilots to enterprise-wide scale.

Nirav Patel: 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.

Speaker #3: 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.

Speaker #3: 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.

Speaker #3: 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.

Speaker #3: 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.

Nirav Patel: Turning now to our business segment performance during Q3, our talent segment continues to reflect the revenue quality discipline we have described on prior earnings calls. Our 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 Q2, 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.

Speaker #3: 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.

Speaker #3: Our average build rate reached another Mastech record high of 92 dollars and 37 cents this quarter, up from 89 dollars 65 cents a year ago, even as we continue to work through insourcing activities from one of our top 10 clients and our own deliberate exit from certain low-margin, non-strategic positions.

Speaker #3: 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.

Speaker #3: 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.

Nirav Patel: 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.

Speaker #3: 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.

Speaker #3: Leveraging AI to extract and reconcile data human-in-the-loop controls and a reusable data foundation are solution is structured to deliver a secure and scalable platform that will streamline audits, support compliance, and help recover overpayments.

Speaker #3: 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.

Speaker #3: In summary, and in our view, 2025 was the 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 through differentiation.

Nirav Patel: 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.

Speaker #3: 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.

Speaker #3: 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 is 2 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, Efficiency is Driving Growth and Expansion, in Q3 2025 with 2 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.

Speaker #2: 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.

Speaker #2: 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.

Speaker #2: 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.

Speaker #2: 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.

Kannan Sugantharaman: 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 outputs. We plan to continue to invest significantly in talent, in building competency, and in expanding our Data and AI business. We believe EDGE positions us well to build differentiated growth around the Knowledge-first Enterprise. I will now discuss our Q3 financial results. During the Q3, 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.

Speaker #2: 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.

Speaker #2: We plan to continue to invest significantly in talent, in building competencies, and in expanding our data and AI business. And we believe edge positions as well to build differentiated growth around the knowledge-first enterprise.

Speaker #2: I will now discuss our third quarter financial results. During the third quarter, we delivered consolidated revenue of 42 million dollars. A 13.4% decrease year over year, compared to the prior year period.

Speaker #2: Our talent segment delivered revenue of 27.9 million dollars, 16.4% lower than the prior year period, though our focus on revenue quality continues. Our build rates once again reached an all-time high for Mastech at 92.37 dollars, up from 89.65 dollars a year ago.

Kannan Sugantharaman: 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 Q3 2025, a 21% reduction. This reflects the same 2 factors we have discussed over the past few quarters, insourcing activity at one of our top 10 clients and our 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 Q4, we expect the seasonal effects that typically affect our industry, including holiday-related slowdowns and year-end contract completions. 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.

Speaker #2: Our billable consultant base declined by 154 consultants since the third quarter of 2025 at 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 our deliberate exit from certain low-margin, non-strategic staffing positions.

Speaker #2: 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.

Speaker #2: Our data and AI segment reported revenue of 14.1 million dollars a decrease of 6.8% compared to prior year period, but a growth of 4.7% sequentially.

Kannan Sugantharaman: On the bookings side, Q3 bookings totaled $10.8 million on a total contract value, or TCV, basis, compared to bookings of $8.4 million TCV in the prior year period. The Q3 is typically a seasonally slower bookings 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 margins terms, we have seen an improvement of 50 basis points from 40% to 40.5%. GAAP net income for 3 months ended 30 September was zero cents per diluted share compared to GAAP net income of $0.9 million, or 8 cents per diluted share in the prior year period.

Speaker #2: On the bookings side, third quarter bookings totaled 10.8 million dollars, on a total contract value or TCV basis. Compared to bookings of 8.4 million TCV in the prior year period.

Speaker #2: The third quarter is typically a seasonally slower bookings period for us, so we view this year over year growth as an encouraging sign. Gross profit of 12.2 million dollars was a decrease of 10.5% compared to the prior year period, but in gross margins terms, we have seen an improvement of 50 basis points from 40 to 40.5%.

Speaker #2: Gap net income for three months ended September 30th was 0 cents per diluted share, compared to gap net income of 0.9 million or 8 cents per diluted share in the prior year period.

Kannan Sugantharaman: Non-GAAP net income for the three months ended 30 September 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 a -$0.03 per diluted share in the prior year period. Non-GAAP net income for the nine months ended 30 September 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 Q3 2026, our liquidity and overall financial position remained solid. On 30 September 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.

Speaker #2: Non-gap net income for the three months ended September 30th was 1.2 million or 10 cents per diluted share, compared to 3.5 million or 29 cents per diluted share in the prior year period.

Speaker #2: Gap net income for the nine-month ended 30th September was 0.2 million or 1 cent per diluted share, compared to a gap net loss of 0.4 million or a minus 3 cents per diluted share in the prior year period.

Speaker #2: Non-gap net income for the nine-months ended September 30th was 3.4 million or 29 cents per diluted share, compared to 6.1 million or 50 cents per diluted share in the prior year period.

Speaker #2: During the third quarter of 2026, our liquidity and overall financial position remained solid, on September 30th, 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.

Kannan Sugantharaman: Our days sales outstanding on 30 September 2026 totaled 56 days, which is in line with our DSO measurement a year ago and an improvement over the last few quarters. During Q3 2026, we did not repurchase any shares of Mastech common stock, and as a result, as of 30 September 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.

Speaker #2: Our day sales outstanding on 30th September 2026 totaled 56 days, which is in line with our DSO measurement a year ago, and in improvement over the last few quarters.

Speaker #2: 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 dollars remains available under the share repurchase program.

Speaker #2: Operator, this concludes our prepared remarks. We will now open the line for questions.

Operator: Thank you. Ladies and gentlemen, if you have a question or comment at this time, please press star 11 on your telephone. If your question has been answered, you wish to remove yourself from the queue, please press star 11 again. We will pause for a moment while we compile our Q&A roster. Our first question comes from Marc Riddick with Sidoti. Your line is open.

Speaker #1: Thank you, ladies and gentlemen. If you have a question or a comment at this time, please press star 11 on your telephone. If your question has been answered and you wish to move yourself from the queue, please press star 11 again.

Speaker #1: We will pause for a moment while we compile our Q&A roster. Our first question comes from Mark Riddick with Sadoti. Your line is open.

Marc Riddick: Hey, good morning.

Speaker #3: Hey, good morning.

Kannan Sugantharaman: Good morning.

Nirav Patel: Good morning.

Speaker #4: Good morning.

Marc Riddick: I wanted to start with some of the commentary around 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 are trying to accomplish there. Then I have a follow-up after that.

Speaker #3: So I wanted to start with, some of the, the commentary around the, the new, the brand and the, the, the commentary in the, the press release regarding the knowledge, first enterprise.

Speaker #3: Maybe you could talk a little bit about, how we should view that and what that means for, for Mastech, how that, impacts your go-to-market strategy, and, and some of the things that you're, you're trying to accomplish there, and then I have a follow-up after that.

Nirav Patel: Thanks, Marc. This is Nirav. I will take that question. 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, 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 records, but much of it actually sits with people in processes, in tacit know-how, and in their judgments.

Speaker #4: thanks, Mark. this is Nirav. I'll take that question. so, so thanks for the question. let me start with the context, because it shapes everything we are doing.

Speaker #4: You know, the pace of, AI innovation, from the frontier labs, is remarkable. You know, but as these models become, widely available, the model itself is no longer what sets an enterprise apart.

Speaker #4: 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.

Speaker #4: 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.

Speaker #4: some of it sits in machines, tools, platforms, systems of records, but much of it actually sits with people in processes, in tacit know-how, and in their judgments.

Nirav Patel: 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. Doing that isn't a single project. It's changing the operating model. Think of it as a well-oiled machine. 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. That's what it means for Mastech going forward, 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. How we go to the market and the engagement we pursue.

Speaker #4: 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.

Speaker #4: Now, doing that, you know, 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.

Speaker #4: 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.

Speaker #4: You know, really, as we support our clients and partners, you know, our three offerings really map directly to that machine. you know, in terms of how we've, we've talked about it.

Speaker #4: So how we go to the market and then 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.

Nirav Patel: we will lead with outcomes, not technology for its own sake. We will help clients codify their knowledge, so it really, really delivers results that can measure. Sorry for a little bit of a longish answer, Mike, but I think that is, Mark, I think it was important for us to really give and provide that context of the Knowledge-first Enterprise.

Speaker #4: So sorry for a little bit of, longish answer, Mike, but, you know, I think that's, Mark, I think it's important for us to really, give and provide that context of the knowledge-first.

Marc Riddick: Okay. I do appreciate that. Then maybe we talk a little bit about the pacing of what you are seeing, particularly with the sequential growth that we are seeing in the Data and AI segment, some of the drivers that you have seen. Maybe you certainly, you have a customer base which is fairly diverse. Just wondering if you are seeing any differentiation in any particular key client verticals or anything that should, that stand out currently?

Speaker #3: Okay. No, I, I, I do appreciate that. And then maybe we talk a little bit about the, the, the pacing. Of, of what you're seeing, particularly with, the sequential growth that we're seeing in the data AI segment, some of the, the drivers that, that you've seen and, and maybe you certainly, you all, you have a, customer base which is fairly diverse.

Speaker #3: I was wondering if you're seeing any differentiation in any particular key client verticals or, or anything that we should, that, that stand out currently.

Nirav Patel: Yeah. I think-

Speaker #4: Yeah, yeah. I think, go ahead. Go ahead, Kannan.

Marc Riddick: Uh-

Kannan Sugantharaman: Uh-

Nirav Patel: Go ahead, Kannan.

Kannan Sugantharaman: No, go ahead, Nirav. Go ahead, please.

Speaker #2: Yeah, go ahead, go ahead, Nirav. Go ahead, 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. Let me take the first part on the deal pacing, right?

Speaker #4: No, I was just, I was just going to, you know, take this in two parts. I think you asked about the, the pacing of, the whole brand and how this is really reflected, and, and, and maybe I can have Kannan chime in on the, the deal win and so forth.

Speaker #4: So, but I let me take the first part on the, 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.

Nirav Patel: To us, the offerings that we talked about today are very fundamental in terms of how this will help enterprises become knowledge-first. 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 is important that everything that you build needs to be production-ready and needs to scale responsibly. Finally, using our Applied AI offerings to convert that strong foundation into business outcomes. This is how we sort of thought about the three broad core offerings.

Speaker #4: 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.

Nirav Patel: 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 for before the agentic world to scale up, I think you need the level of harness, you need a level of foundation that is stable so that they actually get a more predictable growth with agents beyond that. I think it is 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. I do not know, Kannan, if you want to just chime in with your comments on the deal win.

Speaker #4: And for us, the pacing of this, if I look at the clients' prioritization, they, disproportionately, are spending today in areas around data modernization and foundation, I call it, because for before the, the, the agentic, world to scale up, I think you need the level of harness, you need a level of foundation that is stable so that, they actually get a more predictable growth, with agents, beyond that.

Speaker #4: So I think it's interesting to see that from a client standpoint, 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.

Speaker #4: so I don't know, Kannan, if you want to just, chime in with your comments on the, on the deal win.

Kannan Sugantharaman: Sure, Nirav, and Mark, this is a deal which Nirav spoke about. We are really proud of this one.

Kannan Sugantharaman: Sure, Nirav, and Mark, this is a deal which Nirav spoke about. We are really proud of this one. I would say it is our largest AI deal to date. It is one of our existing relationships, which expands that relationship into a very new initiative. The problem we are solving is a common one, right? 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.

Speaker #2: Sure, Nirav. and, and Mark, and, you know, this is, a deal which, which Nirav spoke about. We are really proud of this one.

Kannan Sugantharaman: I would say it is our largest AI deal to date. It is one of our existing relationships, which expands that relationship into a very new initiative. The problem we are solving is a common one, right? 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. Third is the usable data foundation that the client can build on beyond its first use case.

Speaker #4: Mm-hmm.

Speaker #2: I would say it's our largest AI deal to date. and, you know, it's one of our existing relationships, and, and which expands that relationship into a very new initiative.

Speaker #2: The problem we are solving is, 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.

Speaker #2: 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.

Speaker #2: 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 auditors' judgment stays as the center of the process.

Kannan Sugantharaman: Second, human-in-the-loop controls, so the auditor's judgment stays at the center of the process. Third is the usable data foundation that the client can build on beyond its first use case. 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. 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 are then pairing it up with Applied AI, which takes a process from a manual effort to a very measurable business outcome.

Speaker #2: And, and third is the, usable data foundation that the 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.

Kannan Sugantharaman: 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. 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 are 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. Mark, that is how I would reflect on that new opportunity there.

Speaker #2: 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.

Speaker #2: we are capturing the knowledge, the judgment, experienced, by the auditors, and, and we 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.

Kannan Sugantharaman: 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. Mark, that is how I would reflect on that new opportunity there.

Speaker #2: We see this platform, that can grow with client overtime, and not just a one-off project for our clients. So, Mark, that's how I would be reflect on that, that new opportunity there.

Marc Riddick: Okay, great. The last thing that you made mention of, as far as where we are with EDGE and the investments that you are 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. 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?

Speaker #3: Okay. Great. And then the, the last thing you made mention of as, as we, on the as far as where we are with Edge and, and the, the investments that you're looking to, to make going forward and, and adding in talent.

Speaker #3: I, I think the headcount in, data and AI segment was down a little bit. Sequentially, maybe you could talk a little bit about the, the pacing of, of what we might see there, as far as the investments that you see going forward.

Speaker #3: And, do you, do you anticipate gains in, in the, in the headcount in the near future, or is that sort of a, a level setting, do you anticipate a level setting of, of, of, of that, or, or are we or are we moving ahead, from, from a different place, or are you anticipating, you know, a headcount gains going forward from there?

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

Speaker #2: Right. So le-let me address this in, two parts. one, you spoke about Edge. Let me address Edge first. and, and Nirav did comment it about it, right?

Speaker #2: 2025 was our, reset year. 2026 has been the year of putting those efficiencies to work through the investments and building real differentiation and the growth we are seeing in data and AI reflects that and re-reinforces our conviction of what we are doing.

Speaker #2: so that, that's on the Edge front, right? So we are making these investments with a multip multiple view, Mark. we've spoken about this in the past.

Speaker #2: We believe this is the right time to build, capability ahead of demand, and we would, encourage the investors to see them in that light.

Speaker #2: but as you would know, we do have 38 million roughly of cash, and no debt. Which gives us the flexibility to while staying disciplined, but let me also talk about the talent, part of it, right?

Marc Riddick: Right.

Kannan Sugantharaman: Which gives us the flexibility to, while staying disciplined. Let me also talk about the talent part of it, right? You are 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 Q4, we do expect seasonal effects typical to the talent industry, including holiday-related slowdowns and year-end contract completions.

Speaker #2: So you're re right. Our headcount for, September was down, to 580, which was 14 lower the e on a, a quarter on quarter basis, but actually 154 lower year on year.

Speaker #2: 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.

Speaker #2: 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 build rates are at this point in time.

Speaker #2: we believe the impact of those factors are largely behind us. but Mark, you would know the, you know, that as we enter the fourth quarter, we do expect seasonal effects typical to, the talent industry.

Speaker #2: 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.

Kannan Sugantharaman: Beyond that, we do not 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.

Marc Riddick: Thank you very much.

Speaker #3: Thank you very much.

Operator: Thank you. One moment for our next question. Our next question comes from Lisa Thompson with Zacks Investment Research. Your line is open.

Speaker #1: Thank you. One moment before our next question. Our next question comes from Lisa Thompson with Sachs Investment Research. Your line is open.

Lisa Thompson: Good morning. I'd just like you to

Speaker #4: good morning. I'd like you to expand a little bit about, things you've been talking about. First off, the, the 3.0 offerings you talked about, is the are these new capabilities, or are this currently what you're doing and you've just kind of packaged them into distinctive segments?

Nirav Patel: Morning

Lisa Thompson: expand a little bit about things you've been talking about. First off, the three new offerings you talked about, are these new capabilities, or are this 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. 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, really turns what I call enterprise knowledge into solutions, 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 over the last few years. I think if you compare us from a couple of years before to now, right?

Speaker #3: 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, you know, existing work around particularly around MDM as well as customer experience that we have been actually pretty good at in the past.

Speaker #3: How did we fold that into the right place? If you talk about for the three offerings, our applied AI offerings, right, really turns what I call enterprise knowledge into solutions, business, specific business problems, right?

Speaker #3: 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've never did that, you know, o-o-over the last few years.

Speaker #3: 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 DUNs, are, are very, very encouraging for us that these new capabilities are finding, relevancy with our clients and are now starting to, get to certain level of scale, which gives us a feeling that, clients are ready and, and that they want to really accelerate as much as possible with the applied AI, offering that we have.

Nirav Patel: 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. That area of our core work is now folded under what we call it as the AI Governance, Trust, and Safety layer. So essentially, that is what the clients are looking at it.

Speaker #3: The modern data, and we call the modern data because, we have had, like I said, historically, we've really been a very strong, MDM play with IBM as well as, some of the other, partners like Informatica, Wealthio, and so forth, right?

Speaker #3: That area of our, our, 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.

Nirav Patel: I think it is completely AI-fying that core legacy offering. So, I think it is 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 AI Governance, Trust, and Safety layer. So for us, you have newer elements of harness, you have newer elements of dynamic model routing, evals, and everything that comes along with AI is very nicely folding for us in that.

Speaker #3: 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.

Speaker #3: 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.

Speaker #3: You have a newer elements of, model routing, evals, and everything that comes along with AI is, is very nicely folding for us in that.

Nirav Patel: Modern Data is something that we have been more recently building upon, and I think that is 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 have now become the solution partner for their cloud and AI status, really strengthens our positioning to really accelerate Modern Data capabilities. So, that is also a relatively new capability for us. So, these are all new capabilities. That is clearly where we are diverting most of our EDGE unlock or savings to.

Speaker #3: Modern data is something that we have been more re-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?

Speaker #3: And I think our, our Microsoft certification and the expansion that we talked about in our, remarks, is really a true reflection for us, in that regard because, you know, as our partners like Microsoft, where we've now become the solution partner for, for their cloud and AI status, you know, really strengthens our positioning to really accelerate, modern data capabilities.

Speaker #3: So that's also a, 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.

Nirav Patel: I think that is 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.

Speaker #3: 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 pickup as well as in our DUNs.

Lisa Thompson: Okay, interesting. 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?

Speaker #4: Okay. Interesting. So I was when hearing about these, I c occurs to me that maybe these aren't really just projects. Like, is there a possibility of having recurring revenues from some of these abilities?

Nirav Patel: They do, right? Because all things AI are pretty sticky in nature, right, Lisa? 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. As customers start to do more and more of this, they just want more of it. I think these relationships expand further when clients start to add it.

Speaker #3: So they do, right? Because all things AI are pretty sticky in nature, right, Lisa? So, you know, when you go into and we spoke about a DUN with agent e-commerce last time, and, and that's a little bit of a combination of our AI foundation plus applied AI opportunity.

Speaker #3: 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, you know, the traditional model of IT services was extremely input cost-driven.

Speaker #3: 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.

Speaker #3: 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.

Nirav Patel: 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. 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 have began working with this new offering on to large-scale relationships.

Speaker #3: 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.

Speaker #3: So we do believe there's, there is recurs you know, recurring nature of our business, which will help and should help us, strengthen and scale some of our clients that we have began working with this new offering on, to large-scale, relationships.

Lisa Thompson: Hmm. Okay. Are you doing any work as far as trying to optimize customers' use of LLMs? This 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?

Speaker #4: Okay. and are you doing any work as far as, trying to optimize customers' use of A you know, LLMs? This 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.

Speaker #4: Is that something at all you're involved in?

Nirav Patel: I cannot specifically talk about details of the projects, but let me give you a little color, Lisa. The AI governance layer, efficiency layer, the foundation layer, is bringing in what I call optimal use of AI. Optimal use of AI is now becoming extremely critical in all of our client engagements. The board is no longer asking the CEOs and the CTOs of our customers that, "Show me AI," they are 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, "Is it efficient?" We love the magic, but is the magic efficient enough for what it is costing us? That is leading to extremely, what I call, efficient AI deployment conversations.

Speaker #3: So, I, 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.

Speaker #3: And optimal use of AI is now becoming extremely critical in all of our client engagements, you know, 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?

Speaker #3: I mean, we love the magic, but is the magic, efficient enough, for it, for what it's costing us? So that is, leading to extremely what I call efficient AI, AI deployment conversations.

Nirav Patel: Clients are clearly asking us and taking our help and saying, "Hey, how can they efficiently reduce costs?" I think there are two groups of clients that you generally face. There are clients who actually feel the pressures of AI maxing, but there are a lot of clients that do not see AI maxing issues, but they really want more ROI to be better on the AI investments. It is not like that they are running completely short of money to be able to deploy on AI, but the big question mark is, if you are producing something in AI, what is the price am I paying to produce the same outcome? Does the ROI justify that business case for me taking that AI project into production?

Speaker #3: And clients are clearly asking us and taking our help and saying, "Hey, how can they efficiently reduce, cost?" but, you know, I think there are two groups of clients that you generally face.

Speaker #3: You know, there are some AI cli there are clients who actually feel the pressures of AI maxing. but there are a lot of clients that don't see AI maxing issues, but they really want more ROI to be better on the AI investments.

Speaker #3: 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?

Speaker #3: And does the ROI justify that business case for me taking that AI project into production?

Lisa Thompson: Okay. That is good to hear, there is some of that going on. I think, and finally, I would just like you to talk about that project that you were talking 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? Would that be a project you would just move from company to company, or how would that work?

Speaker #4: Okay. That's good to hear. There's some of that going on. I think 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.

Speaker #4: Is that something that you can take to any industry in any company that just pays bills? And would that be a project you would just move from company to company, or how, how would that work?

Kannan Sugantharaman: Let me take that, Lisa. This is something that I did briefly cover. It is, as I said, the problem we are solving is a pretty common one, right? This is where you have the vendor invoice, 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 looks at extracting and reconciling the data. We talk about human-in-the-loop controls, so auditor judgment stays in the process, and that is where there is a reusable data foundation that the client can build on beyond this first case. But your question, we do not see this as one-off.

Speaker #3: Yeah. le-let me take that, Lisa and this is something that, I did briefly cover i-it is as I said, the problem we are solving is a pretty common one, right?

Speaker #3: And this is where you have you know, the vendor, i-invoice the vendor and invoice audit process, which was hugely manual. And dependent on the auditors working through, you know, large volumes of transactions.

Speaker #3: And it was slowing them. And it was meaning an overpayment. What we are building is an a-agentic audit platform which is which is repeatable from a solution standpoint, but i-it largely looks at extracting and reconciling the data we talk about, human-in-the-loop controls.

Speaker #3: So auditors' judgments based in the process. And the third is, reusable data foundation that, the client can build on, on beyond this first, first case.

Speaker #3: But your question, we don't see this as one-off. we every new AI engagement and the solution we build for it adds to our repeatable library of, agentic skills and tools.

Kannan Sugantharaman: 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. We believe 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.

Speaker #3: 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.

Speaker #3: 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, and deliver more value to our clients.

Speaker #3: And, we are pretty excited to see how it scales.

Lisa Thompson: That is great. That sounds really good. Thank you so much. That is all my questions.

Speaker #4: That's great. That sounds really good. Thank you so much. That's all my questions.

Operator: I am not showing any further questions at this time. I would like to turn the call back to Nirav for any closing remarks.

Speaker #2: And I'm not showing any further questions time. I turn the call back to Jennifer for any closing remarks.

Nirav Patel: Thank you, operator. If there are no further questions, I would like to thank you for joining our call today. We look forward to sharing our Q4 and full year 2026 results with you in February.

Speaker #3: Thank you, operator. you know, 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 does conclude today's presentation. We thank you for your participation. You may now disconnect and have a wonderful day.

Speaker #2: Thank you, ladies and gentlemen. This does conclude today's presentation. We thank you for your participation. You may now disconnect and have a wonderful day.