How Equip is using AI to change the way companies hire

Hiring has become a volume problem.

Recruiters post a role and are immediately flooded with applications. Most are irrelevant. Some are borderline. A few, genuinely strong, are buried somewhere in the pile. By the time shortlists are ready, the hiring cycle has already moved on to the next opening.

AI hasn’t really fixed that. If anything, it has added another layer of noise. Candidates now mass-apply using automation tools, resumes are tailored at scale to match job descriptions, and interviews increasingly involve AI assistance in the background. The result? More applications, less clarity.

Bengaluru-based Equip is built around that exact friction point. Founded in 2020 by Jayanth Neelakanta, the platform uses AI to screen resumes, assess skills, and conduct interviews, compressing early-stage hiring into a largely automated workflow. The idea is not just to speed things up, but to identify early, before recruiters get overwhelmed.

More than 820 companies currently use the platform, including Wipro, Delhivery, and Shadowfax. Equip has also raised $400,000 in pre-seed funding from Better Capital in September, 2022.

The market is sizable. India's HR technology sector was valued at $1.2 billion in 2025 and is projected to nearly double by 2034, per IMARC Group. Equip sits across multiple layers of that stack: applicant tracking, assessments, and AI interviews, competing with iMocha, HackerEarth, and Mercer.

From proctoring tool to hiring platform

Equip didn’t start as a recruitment product. Its origins go back to Auto Proctor, an AI-based exam monitoring tool built by Neelakanta in 2020 during the shift to online exams. Built originally as a Google Forms add-on, it scaled rapidly to around seven lakh users in three months, run by a solo founder with a PhD in theoretical and mathematical physics from Syracuse University.

That unexpected traction brought him into Y Combinator. But more importantly, it surfaced a new use case. Companies began using similar workflows for hiring. The overlap between exam integrity and candidate evaluation was hard to ignore.

That transition eventually shaped Equip into what it is today: a platform that moved from proctoring tests to evaluating talent across hiring pipelines

Inside the platform

Equip is structured across four core modules: resume screening, skill assessments, AI-led interviews, and candidate sourcing.

The first layer, resume screening, is free to use. Recruiters set role requirements, and the system begins filtering candidates based on those parameters. Applicants can apply directly or through a LinkedIn integration, answer custom questions like notice period or salary expectations, and receive a Job Fit Score.

That score ranges between 10% and 95%, benchmarked against the entire applicant pool, along with an AI-generated explanation of why a candidate fits, or doesn’t.

The startup says it can process around 100 resumes in five to seven minutes. "We want to do what Amazon does, surface the most relevant result first,” Neelakanta says. 

The second layer is skill assessment, which was the company’s original product. These tests are built for both technical and non-technical roles. A journalist might be evaluated on writing and communication, an accountant on live spreadsheet tasks, and an HR professional on policy-based MCQs.

Tests are auto-generated from a role-and-skill library and are proctored in real time. “We're built for scale. Campus hiring in India means thousands of candidates applying at once; the platform can handle up to 10,000 assessments running simultaneously,” Neelakanta says. 

The third module is interviews. Candidates can go through one-way assessments or live conversational interviews. In the latter, Google’s Gemini powers real-time back-and-forth questioning designed to mimic a human interviewer.

The company claims latency is low enough that candidates often don’t realise they are speaking to an AI. Importantly, evaluation is based only on responses, not tone, appearance, or delivery style.

The newest addition is sourcing. Recruiters can now search a database of pre-assessed candidates based on role, location, and salary expectations. 

“We have around 2.2 lakh candidates in our database; we've already assessed them, so we know what they're good at, what salary they're expecting, what their notice period is," Neelakanta says.

If a candidate is introduced by a recruiter, they are excluded from the sourcing pool for three months, ensuring pipeline ownership remains intact.

The AI layer behind it

Equip doesn’t train its own foundational models. It relies on tools like OpenAI for resume analysis and Google’s Gemini for conversational interviews.

But the way data is handled is tightly controlled. Only role-relevant information is passed into models. If a recruiter marks certain fields as irrelevant, those fields are excluded entirely.

Candidate data is not used to train external models and is not returned to providers.

There is also a strong emphasis on privacy. Unlike traditional proctoring systems that store full recordings, Equip processes video locally and only flags and stores data if a violation is detected. Encrypted storage and time-bound retention policies further reduce the window in which candidate data remains accessible.

Proctoring data is deleted after three months. Interview recordings are stored for recruiter review, but sensitive personal data is filtered out before any AI processing.

From a compliance standpoint, the platform operates as a data processor, while recruiters act as data controllers. Candidates can request deletion, which is executed after recruiter approval.

A newer layer of monitoring also lets candidates sync their phone camera with their laptop via QR code, widening the detection field during assessments.

“Once candidates know they’re being watched, scores go down, not because we catch more cheaters, but because they stop trying,” Neelakanta says.

A lean team, a wide reach

The company operates with just five people: the founder, three engineers, and one marketer. There is no traditional sales team. Most customers come in organically, test the product with free credits, and convert if it fits their workflow.

Equip also runs a weekly group demo every Tuesday, where prospective users can see the platform in action.

Pricing is usage-based. Resume screening is free. Skill assessments cost $1 per candidate. AI interviews range between $1 and $3. The sourcing module charges 5% of the candidate’s CTC only if a hire is made.

Despite the small team, the platform serves over 820 paying customers, with about 65% based outside India.

What comes next

The longer-term plan is to move beyond hiring workflows into something closer to a professional signal layer.

Instead of relying only on resumes, Equip wants to build profiles based on how candidates actually perform across tasks, challenges, and interactions. The aim is to understand skills through behaviour, not just claims on paper.

“We want to do for your professional profile what Facebook and Instagram have done for your personal one," Neelakanta says. “Meta knows what to sell you based on how you interact with the platform. We want to know what job to show you based on how you perform on ours.”

In that vision, hiring becomes less about static CVs and more about continuous data.

The five-year view goes further. “In five years, workforces will be a mix of humans and AI agents," Neelakanta says. "We're not just thinking about helping recruiters find human candidates; we're thinking about what hiring looks like in a world where both exist.”

For now, though, the focus remains narrower: reduce noise, surface signal, and make the first stage of hiring less of a guessing game.