Madhu Modugu and the Work Before the Handshake

In October 2013, Madhu Modugu introduced a recruiting robot. There was no metal colleague arriving at reception, no chair to find, no awkward discussion about who had taken whose parking space. Arya was software, and its assignment was the part of recruiting that can swallow a working day before a useful conversation begins: looking for people.

The promise was to learn from recruiters, remember successful patterns and use that knowledge in the next search. Modugu, then identified as Leoforce’s founder and chief technology officer, described the payoff in plain commercial terms: “more time on relationships and revenue.” The robot had a rather sociable job description. It was supposed to get the humans talking.

Thirteen years later, that small allocation of time remains a useful way into his career. Modugu is Leoforce’s founder and CEO, based in Raleigh, North Carolina. He has built businesses in staffing and technology, taught advanced technology courses, and mentored founders. Across those activities runs a practical interest in what happens when technical capability meets the considerably less tidy business of getting people to use it.

Before Arya, the staffing business

Modugu’s route into recruiting software included helping establish SPECTRAFORCE with Amit Singh and Kamalesh Nayudu in 2004. The partners had worked as Systel consultants. Their new business combined technology consulting and outsourcing, connecting work in the United States with engineering resources in India. Software and staffing were already sharing the same desk.

That setting matters because a staffing company experiences a search problem from both ends. A client needs somebody who can do a particular job. A candidate needs reasons to consider that client. Between them sit the search, the screening, the unanswered messages and the small discoveries that make the original job description seem less complete than it looked on Monday.

Building tools for that environment calls for an appreciation of sequence. Finding a record is one event; establishing interest is another. A system can make the first wonderfully quick and still leave a recruiter waiting for the second. Modugu’s later emphasis on engagement makes sense against this operational background. The purpose of the search was always something that had to happen after it.

There is a quieter personal detail in this chapter. In a 2024 anniversary reflection, SPECTRAFORCE executive Martha Derbyshire recalled Singh and Modugu giving her an opportunity despite her lack of industry knowledge. It is a specific example of a decision that a tidy list of prior experience would struggle to explain: someone had to recognize the possibility of learning.

A robot with a relationship problem

Leoforce’s 2013 launch brought that division of labor into a product. Arya was designed to learn recruiting patterns, while the surrounding software supported sourcing, applicant tracking and social recruiting. The ambition reached beyond running the same search faster. Modugu wanted useful knowledge to survive the individual search and inform the next one.

The distinction became an argument he returned to in public. In 2017, he used the familiar difference between a supermarket self-checkout and online shopping recommendations to explain automation and AI. One speeds up a structured task. The other uses information to make a suggestion. For a recruiter, the equivalent question was whether a tool merely repeated instructions or learned something relevant about candidate fit.

He also supplied a revealing miniature autobiography of technology adoption. Looking back at his old iPod, alarm clock, atlas, instant messenger and desktop computer, he acknowledged that an earlier version of himself would have resisted putting their functions into one large phone. By 2017, he could scarcely imagine doing without it. An advocate for change was admitting he knew the objections from the inside.

“How is the AI making its decisions?”

Madhu Modugu · 2018

The teacher asks the vendor a question

Modugu taught advanced technology courses at North Carolina State University during the early internet period. His public biography also describes mentoring staffing companies and technology startups. Teaching offers a useful parallel to his business writing: the explanation has to travel from the person who understands a system to the person expected to make a decision about it.

In 2018, he set out a buyer’s test for AI vendors. Establish what the vendor means by AI. Ask why the problem needs it. Then ask, “How is the AI making its decisions?” Those questions place a burden on the seller to explain the mechanism, the business purpose and the route from data to result.

It is an interesting position for someone selling AI himself. Taken seriously, the questions apply to his company as readily as to anyone else’s. A handsome interface would have to wait its turn while the buyer asked about validation, bias and learning over time. The demonstration might be dazzling. There was still homework.

The classroom connection should not be mistaken for an academic origin story. Its interest is more immediate: a founder explaining technical ideas to working buyers must decide which distinctions help people act. Modugu’s recurring distinction is between having a technology and knowing what useful work it performs. That is a demanding question precisely because the answer ought to be understandable.

The people a résumé might miss

His colleagues’ recollections give this technical career a more personal scale. In a 2022 departure reflection, Leoforce employee Rajarishi Punukollu described five years moving from SQL queries and support tickets into databases, product decisions and ownership. He remembered discussions with the CEO about future features and credited Modugu with encouraging people to do their best work.

A separate public message from Sreevally Pasumarthy thanked Modugu for trusting her with a role she wanted to pursue when she was uncertain about the difference she could make. These are individual recollections, rather than a complete account of company life. Their shared detail is opportunity: a leader making room for someone to attempt work beyond an established description.

That detail puts a human complication inside the recruiting problem. If a career can grow through a chance taken by another person, past experience cannot be the entire story. A candidate’s record matters, but so does the possibility of doing something that record has not yet had a chance to contain.

Modugu has expressed a related ambition for Leoforce: connecting people with work that gives them a sense of purpose. Software can assist a search and organize the evidence around it. The aspiration still concerns what the person experiences after being hired. That stretches the founder’s definition of success beyond the satisfying moment when a vacancy disappears from a dashboard.

When the search becomes a conversation

The product history followed the same direction. In November 2016, Leoforce introduced Mover Predictor, which classified the likelihood of a candidate changing jobs as low, medium or high. The feature was presented as a way to help recruiters decide where to spend their time. Its premise recognized a familiar frustration: being qualified for a job does not automatically make someone interested in moving.

In February 2019, Arya 3.0 combined sourcing from internal databases, job boards and social networks with candidate engagement through phone, text, email and bots. The scope had expanded from locating people toward contacting them within the same platform. A list could now have a route into a conversation, although the candidate still had every right to ignore the invitation.

Modugu’s public discussions continued to circle the quality question. In April 2024, he joined TAtech CEO Peter Weddle for a webcast about identifying strong candidates and predicting their success. The topic fits the larger pattern. Volume is easy to describe. Whether a person and a role will work well together is the harder proposition, and the one that gives all the searching a point.

The second job: making AI usable

Leoforce’s February 2025 announcement described a shift from a single AI product toward customizable services covering job advertising, sourcing and candidate engagement. It explicitly acknowledged the difficulty organizations faced in adopting recruiting AI. The company was broadening the offer to include the expertise needed to use the technology, with hiring outcomes as the commercial focus.

Modugu put the continuity plainly: “Our mission remains unchanged: to help organizations achieve better, faster hiring outcomes.” The change in delivery is revealing. A founder can spend years improving what a product does, then discover that the customer also needs help fitting it into the working day. Adoption has its own engineering problems, only some of which involve code.

He addresses a similar problem at Bridgera, which lists him as CEO and founder. In a public post about its AI readiness framework, he warned that companies could rush into pilots before understanding their data, infrastructure and talent. The vocabulary had widened to enterprise AI, but the concern was familiar: an investment needs an organization capable of putting it to work.

The promise acquires paperwork

In October 2026, Leoforce announced ISO/IEC 42001:2023 certification for its AI management system, issued by SGS North America. The stated scope covered the design, development, deployment and operation of agentic and non-agentic recruiting AI. It marked a shift in the questions surrounding the business: alongside what the technology can accomplish sits how its development and use are governed.

For Modugu, that brings an older argument forward. The vendor questions he raised in 2018 included how results are validated and how bias is addressed. Certification gives the governance discussion a formal structure. It does not decide whether two particular people should work together, or turn every prediction into a fact. Those judgments still deserve attention.

There is a pleasingly unglamorous continuity here. The 2013 robot was meant to rescue time for relationships. The later services addressed the work of adoption. The governance milestone addressed confidence in the machinery. Each adds another condition to the original promise: the tool has to help, people have to be able to use it, and they need reasons to trust it.

Modugu’s career sits inside those conditions. The staffing founder, technology teacher and AI executive are dealing with different versions of the same difficulty: making a connection possible without pretending the connection is automatic. Arya can search. A company can organize. A founder can create an opening for someone. Eventually, another human being has to answer.