Jack and Jill raises $40M to build an agentic hiring network with half a million users across SF, NYC, and London
Key Points
- Jack and Jill raises $40M Series A led by Aeslie Capital to scale its AI-agent hiring network to half a million users across SF, NYC, and London.
- The platform deploys two agents, Jack and Jill, that match job seekers to companies directly, bypassing a recruiter and application layer now flooded with AI-generated noise.
- Companies pay on success-fee or SaaS models while candidates use the platform free; Wilson sees the highest value in agents continuously monitoring the job market rather than negotiating offers.
Summary
Jack and Jill has raised a $40M Series A led by Aeslie Capital, with Madrona Ventures and Kriandan participating. Kriandan also led the company's previous $20M seed round and doubled down in this raise. The San Francisco-based startup has built what Wilson describes as the largest agentic social network in the world, with close to half a million users across San Francisco, New York, and London.
“We built two different AI agents — Jack that works for professionals helping them find their dream job, and Jill that works for companies helping them find amazing people to hire. Together we operate the largest agentic social network in the world with close to half a million people across San Francisco, New York, and London.”
How it works
The platform runs two AI agents: Jack, which represents job seekers, and Jill, which represents hiring companies. Rather than replacing the application or recruiter outreach process, the agents communicate with each other to surface high-signal matches, including for candidates who are only passively open to new roles. Wilson's framing is that as AI has driven down the cost of outbound recruitment on both sides, the market has become flooded with noise, and the only structural answer is to have agents manage the connection layer directly.
The signal-to-noise problem Wilson describes is measurable. Average job postings in 2026 are receiving more than double the applications they did a year earlier, driven largely by AI-assisted mass applying. Some companies, he says, have stopped posting roles publicly to avoid the volume. Recruiters using first-generation AI tools are running the same playbook in reverse, sending AI-generated outreach at scale to candidate lists.
Business model
The consumer side is free. Companies pay, primarily on a success-fee model, with some retainer-based SaaS options available. Wilson doesn't break out revenue figures.
Where the product sits today
Beyond matching, Jack offers comp benchmarking and interview prep for candidates through to offer negotiation. Wilson sees the highest-value use case at the top of the funnel, where an agent can continuously monitor the entire job market on a candidate's behalf, rather than at the later stages of negotiation. He leaves open the possibility that agent-to-agent offer negotiation becomes viable eventually, but doesn't treat it as the near-term priority.
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