Consider a market in which a highly credentialed seller makes five to six hundred offers over two and a half years and receives one expression of interest. In any other context, capital, real estate, procurement, that conversion rate would trigger an immediate review of the go-to-market approach. In the senior talent market, it is routinely absorbed as personal failure.
That was the position Robin found himself in. Twenty years of experience, much of it in senior management; a history of building teams from nothing and repairing profit-and-loss statements no one else would touch; a title that once opened doors simply by sitting on a résumé. He assumed success would keep finding him, not out of arrogance, but on the evidence. Then the phone stopped ringing, and he responded the way most professionals do: he applied. Five or six hundred times.
"Out of five hundred or six hundred applications, I received one interview, probably one or two, max," says Robin, now a Director of Delivery.
The founder who eventually worked with him argues that this outcome is neither bad luck nor, on its own, proof of a broken market. It is what happens when a highly capable person with no strategy walks into a room that three thousand equally capable people are also walking into.
"The market doesn't reward the most capable person in the room. It rewards the one with the better strategy," says Major Richik, Founder and CEO of NxtJob.ai, a serving officer in the Indian Army who previously built the recruitment-tech venture HyreSnap.
His central thesis is an operating one: for experienced professionals, the job search is itself a second job, stacked on top of the role they hold or the one they have just lost. Managed as a hobby, a few tired clicks after dinner, it produces hobby-grade returns; the market ignores the candidate quietly and completely. Robin had already paid for two programmes that told him to tweak his résumé and wait; neither worked, and he nearly declined to trust a third. NxtJob.ai, he says, changed his perception of the search altogether, and, according to the company, ultimately placed him as Director of Delivery, a rung above the positions he had spent years applying for.
"I did not have any strategy. I don't know how to approach the job market, I simply went ahead and applied. After getting into the job search properly, my complete perception changed," Robin says.
What follows is the company's method, examined as four distinct market problems.
The first reader is software
The initial inefficiency sits at the point of screening. Most candidates treat the résumé as a summary of a career; in practice, it is a piece of software's first impression of them. Applicant tracking systems evaluate candidates in seconds, on formatting and keywords, before a recruiter ever sees a name, which means a document optimised for a human reader may never reach one.
Devjit's experience illustrates the cost of that mismatch. He came to the company at 54, seven months out of work and, by his own account, heartbroken and exhausted, having spent those months sending résumés into portals and inboxes he suspects never reached a human being. The constraint was not capability, Major Richik contends; it was a document built for a person rather than the algorithm reading it first.
The inverse error is equally costly. Srinivasan attempted the contemporary shortcut, feeding his résumé to ChatGPT with instructions to "optimize" against a job description. The output cleared the screening layer, on the strength of achievements he had never had.
"It will throw something on me and interviews will be scheduled. But I would not be able to live up to the interviewer's expectations, because it's all fake. It was embarrassing, to say the least," he says. The padded document got him into the room; he did not survive five minutes inside it.
The company's answer replaces file-editing with an asset-and-instance model: one exhaustive "master résumé" capturing every project, number and achievement, from which a fresh, role-specific version is generated for each opening. Two AI agents divide the work, Navigator maps the career into the master document; Tailor produces the customised pitch per role.
The unposted majority
The second problem is one of discovery, and it is the claim that most unsettles clients. A widely cited estimate in career-coaching circles holds that a large share of desirable roles, by many estimates as much as 70 percent, are never publicly posted: not on LinkedIn, not on a job board, nowhere a routine search would reach. If accurate even approximately, the visible market on which candidates compete is a fraction of the real one.
The commercial logic behind the opacity is straightforward, in Major Richik's telling. The moment a senior role is posted, thousands of applicants, some deploying automated bots, flood the listing, and filtering them, even with an ATS, costs weeks of human effort. Employers rationally route around that cost: the roles are filled through people, a department head, a referral, a phone call between two professionals who trust each other.
"While you're refreshing job boards at midnight, the role you wanted was filled by someone who never applied. They simply got introduced," he says.
On the findable side of the market, coverage itself is a solvable problem. A third agent, Hunter, searches past the obvious job boards into company career pages, Boolean queries and freshly posted listings, including the taxonomy problem of titles. "Nobody calls your job by the same name twice," Major Richik notes: a product manager may be advertised as a product owner, a platform lead or a growth lead.
Distribution: relationships as channel
Access to the unposted majority runs through people, which makes networking the decisive channel, and the most poorly executed one. Major Richik is scathing about the default approach: fifty connection requests carrying a note that reads "Hi, can you refer me?" Effective networking, he argues, means identifying the two or three individuals who actually sit inside a target company and building a relationship genuine enough that they would attach their own name to the candidate's. Even conversations that convert to nothing immediately compound into future referrals, someone changes companies, someone gets a budget, someone remembers you.
A fourth agent, Networker, identifies the relevant contacts and follows up "the way a careful professional does, not a desperate one." The differentiating skill, in the founder's framing, is reversing the perceived favour, making the recipient feel the outreach benefits them. The company codifies this as the WIN Method: a Well-researched problem, an Insightful solution, and a Narrative that ties the two together.
The underlying premise reframes the transaction entirely: beyond a decade of experience, interviews stop being interviews and become meetings, two professionals deciding whether to work together. No executive would enter a client meeting without researching the client's problem; the next role is the most important deal of a career and deserves no less preparation. A fifth agent, Pitcher, researches the specific problems a target company faces and packages a problem-solution narrative the candidate sends directly to the decision-maker who owns it, bypassing the recruiter and the application inbox.
"It turns 'please consider me' into 'here's what I'd already started fixing on day one.' You're not sending applications anymore. You're sending proposals," Major Richik says.
The last mile: performance and value capture
Two stages remain, and both are execution problems. Inside the room, the variable is composure, calm confidence versus the faint smell of desperation. A sixth agent, Interviewer, runs structured mock interviews with feedback and STAR-based storytelling, rehearsed until composure is instinct rather than performance. Robin credits precisely that preparation, he went through the material "ten to fifteen times", for entering a final round for a Technical Program Delivery Manager role and leaving with an offer for the more senior Director of Delivery position. Srinivasan, who had lost offers at PwC and elsewhere before joining, identifies the same inflection: "That is when I understood it is the face-to-face practice which was missing. That was the game-changer."
The final stage is where professionals most often leave value on the table: the offer. Major Richik's counsel is not to celebrate or sign immediately, acceptance runs both ways. He claims recruiters routinely hold 30 to 40 percent more budget than their opening number, and cites an estimate that a professional can forgo close to ₹8-10 crore over a lifetime by never learning to negotiate. A seventh agent, Negotiator, benchmarks what a role is actually worth and rehearses the counter-offer before it is needed. (These figures are NxtJob.ai's own estimates.)
An operating model, not a tip sheet
The founder is candid that the venture is personal. He built NxtJob.ai, he says, not because résumés interest him but because he watched capable people lose, first to a filter, then to silence, then to a process he believes was never designed to recognise them, and he frames the mission in the language of his Army training: an ethos of helping the deserving who stand to lose from the system.
The full platform runs nine AI agents alongside human consultants, more than can be covered here, and Major Richik points prospective clients to a two-day weekend bootcamp where he walks through the complete method used with clients such as Robin, Srinivasan and Devjit.
His closing argument is addressed to the senior professional who assumes a track record still speaks for itself: you did not reach this level by being unprepared. You got here with a strategy, every single time. This is not the moment to break that streak.
