For high-growth hiring teams, huge application volumes are now just the cost of doing business. They’re regularly seeing hundreds, sometimes thousands, of applications per role. And the rise of AI-assisted job applications has only accelerated things, making it harder for teams to identify genuinely qualified candidates quickly and confidently.
This is a recruiting environment where traditional manual review processes break down. And for the recruiters themselves, the experience can become both exhausting and ineffective.
Nscale, an AI infrastructure company building data centres across Europe, the US and Asia, has nearly 300 roles open around the world. Automattic (the company behind WordPress, Tumblr and WooCommerce) sees enormous inbound application volume even without promoting roles on LinkedIn.
Both companies need an effective way to precisely assess every new application, without scrolling through one by one. That has to go beyond automated filters and knockout questions, to truly understand what great looks like in their organizations.
So they implemented Metaview’s Application Review agent. Both had experimented with AI-powered tools before. Here, we see why Metaview was the one that stuck.
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AI gladly does the jobs recruiters hate
At the volumes both Automattic and Nscale see every day, the evaluation process can easily become a matter of just getting through the pile. Recruiters need to devote hours and hours to manual screening, or simply accept that they can’t give everyone a fair assessment.
I was spending days just looking through CVs and not giving them enough time, if I’m brutally honest.”
At Automattic, a single role generated more than 1,000 applications over one weekend, without even being posted to LinkedIn. “And if I put it on LinkedIn,” says Recruiting Director Amandeep Shergill, “I'd get five times that number of applications and with zero increase in quality; probably a decrease in quality.”
“I probably spent two or three days looking at profiles,” says Nscale’s Samir Yeghni. “We were just freshly onto Greenhouse, our ATS system. So I was doing it the old manual way and I was spending days just looking through CVs.”
The difference with Application Review is night and day. The AI agent handles all of this manual, repetitive work.
I actually really enjoy looking at good quality CVs. But I really don't like looking at CVs that have no relevance to me. So, certainly using the [Application Review] tool has really helped, and the time saving has just been incredible.”
Crucially, Application Review highlights worthy applications as soon as they arrive. This lets Amandeep and Samir focus more on evaluating the strongest candidates, speaking with hiring managers, and making better hiring decisions.
We can actually do more meaningful work and let these AI agents do the dross, the stuff that we don’t want to be doing.”
Both recruiting leaders stress that moving faster is just one benefit. More value comes from being able to maintain quality and focus in the face of overwhelming application volume.
Speed helps, but accuracy really counts
When the goal is cutting candidates from the pile, most TA teams turn to basic screening automation. That will immediately find incomplete profiles, irrelevant applications, or people who don’t have the necessary experience.
But it’s now easier than ever to game the system. AI tools write convincing, role-specific CVs in seconds. Meanwhile, filters eliminate a lot of great fit-candidates who just didn’t use the right keywords.
Classic automation is severely limited. But AI Application Review brings a new level of nuance and understanding. The agent digests and learns from the context you feed it. Every candidate passed or progressed, and every offer extended feeds back into the decision engine.
At Nscale, Samir says that 70–75% of candidates surfaced as “good” or “great” by Application Review progress to the hiring manager stage. “The conversion rate is absolutely fantastic.”
Amandeep gives a specific example. “I was working on finding AI engineers, but getting machine learning engineers. I don't want machine learning engineers. This is something that Metaview picked up very quickly and adjusted to.”
Classic filters may be fast, but they don’t learn. And at the end of the day, your hiring never actually improves.
“Other tools say they’re learning,” says Amandeep, “but they don’t tell you what changes they’re making. With Application Review, you're actively seeing the changes, and you can edit those changes. You're really in the driving seat.”
The system doesn’t just screen out the bad fits. It pinpoints genuine talent early in the process, so recruiters can make a connection, schedule interviews, and get to the offer stage faster than ever.
We've hired three or four candidates through Application Review. One came through Application Review only three weeks ago, and they already joined my team this week. I used application review exclusively for that search. It's really working well.”
Adoption and time to value are critical
Both Automattic and Nscale have tried to roll out recruiting AI in the past. But after the initial excitement, Samir and Amandeep agreed that they never managed to truly operationalize AI internally.
Recruiters and hiring managers are time-poor, explains Amandeep. “If they have a lot of friction when they’re starting to get set up on a tool, they’re just not going to use it.”
While other tools regularly take hours to calibrate a new role properly, Metaview only takes a few minutes. “I got the job active and Metaview did its thing. I reviewed between five and 10 profiles, gave it some feedback and immediately the ideal candidate profile was updating itself. That was a game changer.”
With Application Review, we’re seeing a significant improvement in the amount of time spent finding and surfacing the best people.”
Platform consolidation is another major consideration. Nscale initially chose Metaview for interview transcription. But they quickly saw value in managing multiple workflows within a single system.
“We love to have one platform that we’re using for multiple different things,” says Samir.
Organizations must be able to integrate AI into recruiting workflows in a way that recruiters actually trust, understand, and want to use. And without creating tricky data silos and sprawling TA stacks.
We work really hard to make the product as approachable as possible, especially for people who are very time poor. They’re critical in the workflow, but don't own the workflow, so you want them to have a really easy product that adds value.”
An AI-driven transition in recruiting teams
AI recruiting platforms aren’t just becoming more common in TA teams. They’re actually transforming the skills and expertise required in organizations.
Samir already sees Talent Operations becoming increasingly important as AI tools are embedded in workflows. “There will be a shift away from TA partners, to TA operations specialists who are going to be managing brilliant tools like this.”
As AI systems take over more repetitive execution work, recruiters are increasingly moving toward orchestration, calibration, stakeholder management, and strategic decision making.
In other words, the role becomes less about processing volume and more about managing intelligent systems effectively. That means redesigning workflows to feature AI assistance, creating shared context across hiring systems, and empowering recruiters to intervene where human judgment adds the most value.
But most importantly, we’re getting away from the grind—the “dross,” as Amandeep calls it.
Recruiters can have a more meaningful impact, not just sit there clicking hundreds of times. They can go and look at the good CVs. They can talk to really interesting candidates. And they can go and talk to hiring managers to really understand the business in more quality.”
Recruiting teams need to find signal in the noise
For companies hiring at scale, inbound volume isn’t the problem. It’s identifying the right candidates quickly enough for recruiters to act on them.
Both Automattic and Nscale have seen this at scale. And Metaview’s Application Review has completely changed how their recruiters operate.
Instead of manually clicking through hundreds of irrelevant CVs, they focus on:
- Engaging stronger candidates,
- Partnering more closely with hiring managers,
- And making better hiring decisions, faster.
That transformation was fast and efficient. Adoption is widespread, hiring workflows are consistent, and candidate quality is excellent.
In a world where application volumes are exploding, but quality applications are few and far between, Automattic and Nscale are seeing the results of smart AI recruiting.
To see how AI-powered Application Review can transform your hiring process, try Application Review for free.
Bring Metaview into your hiring stack.
Live notes, structured scorecards, and ATS sync - set up in under 10 minutes.
