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Deal Terms
| Item | Value |
|---|---|
| Company | Metaview (London, UK / San Francisco, US) |
| Round | Series C |
| Amount | $60M |
| Lead | Insight Partners |
| Participants | GV, Intrepid Growth Partners, Seedcamp, Vertex Ventures US, Plural, Garuda Ventures |
| Valuation | Undisclosed |
| Total raised | $110M |
| Founded | 2018 |
| Headcount | About 80 today, plans to grow to 250 within a year |
What the Company Does
Metaview automates the recruiting process — it started by using AI to record and structure interview notes, and has since expanded into autonomous agents that execute most of the operational work in hiring directly.
Its core product is a set of recruiting agents: they source candidates, screen resumes against a role brief, write and send outreach, and run structured initial screening conversations. The company says humans still set the criteria and make every final call — the agents handle the repetitive, time-consuming steps in between. The headline product in this round is "fillmore," positioned as an autonomous recruiting coworker that sources candidates, writes outreach, manages follow-ups, and books screening calls end to end; the company is also building a dedicated AI screening agent. Co-founders Siadhal Magos and Shahriar Tajbakhsh (formerly of Uber and Palantir) started the company in 2018 by capturing and transcribing interview notes, and have since recorded more than 6 million interviews.
More than 7,000 companies use Metaview today, including Deel, Affirm, Navan, and Replit; the company says some customers have cut time-to-hire by more than 75%. It acquired Reval, another AI-native recruiting startup, in August, and plans to open a New York office alongside its existing London and San Francisco teams.
What This Round Signals
What It Means for the Agent Ecosystem
Recruiting is a setting where candidate volume routinely outstrips an organization's capacity to process it, but the steps themselves are fairly well defined — exactly the combination where agents tend to land first in enterprise settings. Metaview's framing echoes the trajectory of coding agents directly: "the best engineering teams have already moved from writing code by hand to orchestrating AI agents that do the work. Recruiting is next." It also signals that competition among vertical agents is shifting from "which task can be automated" to "who can stitch the whole process into a system that makes decisions on its own, instead of just assisting a human note-taker."
What Investors Are Betting On
Insight Partners managing director Ryan Hinkle's comment spells out the thesis directly: "the recruiting process is generating more candidate volume than most organizations know how to handle" — a supply-demand gap big enough that hiring more people can't close it; only agents adding capacity can. The same week, two other recruiting-agent startups also raised money — Spott ($21M Series A led by Balderton) and Jack & Jill ($40M) — a sign that several institutional funds are placing bets on this sub-sector at once, not just one fund acting alone.
Numbers Worth Watching
- $110M in total funding against more than 7,000 paying enterprise customers — a large customer base relative to most early-stage vertical agent startups
- In one case the company cites, an agent went from sourcing to a signed offer in 30 days, autonomously contacting and researching 52 candidates and booking 5 screens along the way
- Plans to grow from 80 to 250 people within a year — nearly a 3x headcount increase, suggesting the company is betting on execution capacity, not just the size of the raise
Watchlist Status
Metaview is not currently on the watchlist. Recommend adding it under section D3 (HR / Recruiting), alongside Eightfold AI, Findem, HireVue, Mercor, and Phenom — tracking whether an autonomous recruiting agent can hold its claimed time-to-hire and conversion gains as it expands from point automation into a full end-to-end process.
Today's Takeaway
Recruiting AI coverage has mostly stayed at "it takes your notes, it schedules your calls." Metaview's round states the goal more directly: let the agent execute the sourcing, outreach, and screening itself, while humans only set criteria and make the final call — the same curve coding agents rode from autocomplete to autonomous execution, just applied to a different labor-intensive vertical.
References
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