$35M Series B led by GV: Metaview's interview intelligence platform and the four AI agents inside it
Metaview just closed a $35M Series B led by GV (Google Ventures), with continued backing from Plural, Vertex Ventures, Seedcamp, and Coelius Capital. True Equity, Victor Riparbelli (founder of Synthesia), and Barney Hussey-Yeo (founder of Cleo) joined the round. The total to date is $50M, against 3,000+ customers and more than 3 million interview conversations captured.
The bet from GV and the existing investors is not on a slightly better notetaker. It is on interview intelligence as a category, and on the platform that turns every captured hiring conversation into a structured, queryable source of signal that compounds across the search, the team, and the company.
The rest of this post is what the raise actually buys. Four AI agents under one signal layer. A trust posture that is now SOC 2 Type II certified. A milestone trail that runs from a 2019 founder thesis through the AI-native rebuild in late 2022 to the 2026 platform. And the operational outcomes the customers already living inside that platform are reporting today.
Why this raise is a bet on interview intelligence
Hiring software has historically been a workflow problem: get the job posted, get the candidate scheduled, get the offer sent. Interview intelligence is a different problem. It is the question of what actually happened in the hour the recruiter, the hiring manager, and the candidate were in the room.
That hour is where the signal lives. It is where the must-haves get committed, where the trade-offs get surfaced, where the consensus call gets earned or lost. For two decades the recruiter has been the human transcriptionist of that hour, with the structured output of the conversation reduced to whatever they could type while the conversation kept moving.
Captured-conversation data changes the question. Once the interview is a structured artifact instead of a memory test, the must-haves can carry forward into screening, the per-competency notes can carry forward into debriefs, and the cross-panel signal can carry forward into the offer-prep brief. The recruiting team stops re-creating context every stage and starts compounding it.
According to Metaview's 2026 AI & Hiring Alignment Report, surveying 505 recruiting leaders and hiring managers across North America and EMEA, teams that put AI at the core of hiring are 3.8x more likely to rate the recruiter / hiring-manager relationship as excellent (55% vs 14% baseline). The platform-level outcomes follow that pattern. The stat row below is what 3,000+ customers across 3 million+ interviews are reporting, not a forecast.
The four AI agents under one signal layer
The most important thing the raise unlocks is not a fifth point product. It is the connective tissue between the four product surfaces Metaview already ships, so each one reads from and writes to the same captured-conversation context layer.
A point product is bought once, used by one persona, and replaced when a sharper version of the same point ships. A platform is bought once and compounds. Every new surface added on top of the same data layer reads the calibration the intake committed, the rejection reasons Application Review fed back into the ATS, and the per-competency notes captured at the panel stage. The four agents below all live on that layer.
Joins the interview, captures the conversation, and ships the structured per-competency notes the moment the meeting ends. Across 3M+ interviews to date.
Triages every inbound applicant against the ICP committed at intake. Great-fit and good-fit buckets, fraud-detection flags, daily digests, ATS write-back.
Natural-language candidate search across the public web, your ATS, and your own captured Metaview conversations. Push the result back into Ashby, Lever, SmartRecruiters.
Insights across captured interviews, filterable by AI columns. Where did the panel agree? Where did they not? Which interviewers carry the most calibration weight?
Each agent on its own would be a solid product. Together, with the data layer underneath, they are the platform GV invested in. The intake calibration informs the Application Review ICP, which informs the panel kit, which informs the per-competency notes the Notetaker structures, which inform Reports, which inform the next intake. Nothing has to be re-input. The agents work for each other, not just for the recruiter.
We have conversations around specific numbers and metrics. Being able to bring these to the leadership team during the decision making process has been a really huge help.”
Hiring used to run on vibes: the milestone trail that changed that
It is worth being honest about where the bar was. Hiring at most companies, until very recently, ran on vibes. Ten people on a hiring panel would give ten different answers to "what makes a great hire here." The data that would actually settle that question was trapped in the recruiter's head, in the hiring manager's Slack DMs, and in the scorecards nobody wrote down in time.
My co-founder Shahriar and I saw that at Uber and at Palantir. Hiring was the most strategic line item in both businesses and one of the least instrumented. When we started Metaview in 2019, the thesis was that the interview conversation itself was the unit of signal worth capturing. When GPT-3.5 shipped in late 2022, we rebuilt the entire platform AI-native to make that capture useful to the recruiter in the room.
The chronology below is the trail. Five dimensions, three time slices, the path from a 2019 founding thesis through today's 3M-interview footprint to the next 18 months the Series B funds.
| Dimension | Then (2019, founding) | Now (2026, Series B) | Next 18 months |
|---|---|---|---|
| Capture surface | One-on-one candidate interviews, manual notes | Interviews, intake calls, debriefs, screens, all auto-captured | Cross-loop signal across every hiring conversation in the org |
| Product surface | Notetaker (single product) | 4 AI agents on one signal layer | Agent-to-agent orchestration, agent-managers as a TA role |
| Customer count | Single-digit early customers | 3,000+ customers, 3M+ interviews captured | Enterprise depth and global reach, US + EU residency |
| Trust posture | SaaS-default controls | SOC 2 Type II certified, EU/US residency, Transient Mode | Continuous Type II + adjacent certifications on the trust roadmap |
| Funding state | Pre-seed (Seedcamp, Plural) | Series B, $35M led by GV, $50M total to date | London + new San Francisco office, hiring across eng, GTM, ops |
None of the columns get there without the columns to their left. The 2026 platform is built on the 2019 founding thesis. The next 18 months are built on the 2026 footprint.
The trust layer under the raise
The trust layer is worth naming explicitly, because it is the thing that lets the interview-intelligence story actually run inside an enterprise. Captured audio is denser data than the structured HR records most SaaS controls were designed around. We built for that posture from the start.
Metaview is SOC 2 Type II certified, with the controls tested continuously across the full audit window. EU customer interview content stays in EU regions; US customer content stays in US regions. Transient Mode is available for sessions that should produce structured notes without ever persisting the raw audio. Customer admins, not Metaview, control who in their org can see transcripts.
What customers are already getting at scale
The platform-level outcomes in the stat row above are the average across the 3,000+ customer base. The named-customer outcomes are where the story gets specific. The high-volume hiring operation at Engine, the modern travel platform connecting nearly every hotel, airline, and car rental in the U.S., is a useful reference for what the platform looks like inside a 700-person company doing real-volume hiring on Greenhouse and Zoom.
Laura Stapleton, Engine's VP of People, brought Metaview in to reduce the time her team was burning on writing screening notes and chasing scorecards. The team is now ~40 minutes lighter per recruiter per day on that admin layer, across 8,400+ captured interviews to date. The card below has the headline numbers.
Engine is one shape of customer. Brex, Deliveroo, Quora, Deel, and True are others, each running the platform against their own stage, scale, and hiring philosophy. The thing they have in common: the layer they buy is the captured-conversation layer, and the agents are what runs on top.
What we're shipping next, with you
The thing the Series B funds, beyond the platform engineering, is the team that ships it. Metaview is growing the London office and opening in San Francisco, hiring across engineering, go-to-market, and operations. If the platform shape above is the kind of category-defining product you want to help build, the open roles are here.
Alongside the product roadmap, the raise also funds the community side of the work. The 10x Recruiting community is where the forward-thinking recruiters, RecOps leads, and TA pros already running the AI shift are sharing what they've learned, where the boundary between human judgment and agent execution is, and what the next role on the team looks like. The community side compounds the product side: the better the practitioners get at running this, the better the product gets at supporting them.
If you're a current customer reading this: nothing about the raise changes the commitments you already have. Same data, same controls, same pricing tier. The new capital deepens what we ship, not what you already pay for. If you're not a customer yet: the platform sets up in under 10 minutes on every supported ATS and video stack, and a 15-minute live walkthrough is on the demo page below.
How to be a better recruiter: use Metaview. No doubt why you are growing so quickly. The product you are developing is the future of recruiting. Helping us to be more present during conversations and improving interview processes is a significant efficiency boost.”
MetaviewSee the four agents on one signal layer, set up against your ATS in under 10 minutes.
Frequently asked
Who is GV and why did they lead this round?
GV is Google Ventures, Alphabet's independent venture arm. The lead investor on a Series B sets the round terms and signals conviction in the category and the team. GV's portfolio in AI-native enterprise infrastructure made them a natural fit for an interview-intelligence platform built on captured-conversation data.
What changes for current Metaview customers?
Nothing changes about your contract, your data, your pricing tier, or the people you work with at Metaview. The capital deepens the product roadmap, opens a San Francisco office alongside the London team, and accelerates the four AI agents on the signal layer. Same trust controls, same SOC 2 Type II posture, same regional data residency.
What's actually shipping in the next 18 months?
More signal across more meeting types, deeper ATS write-back, agent-to-agent orchestration so the intake's calibration carries forward into Application Review and AI Sourcing without re-input, and continued investment in the trust roadmap. The customer success and product teams will share the specifics on roadmap calls.
Is Metaview hiring?
Yes, across engineering, go-to-market, and operations in both London and San Francisco. The open roles are at metaview.ai/careers. We're particularly interested in operators who've seen the inside of a fast-growing recruiting team or a fast-growing AI-native product.
What about EU customers' data?
EU customer interview content stays in EU regions. US customer content stays in US regions. No cross-region transfer of raw audio or transcripts. That residency boundary is a SOC 2 Type II audit-tested control, not a configurable setting.
Can I see the platform live?
Yes. Book a 15-minute walkthrough at metaview.ai/demo and the Metaview team will run it on a representative meeting type from your hiring process: an intake call, a screening interview, or a debrief. You'll see the structured output before the call ends.
