Jack & Jill has raised $40m in Series A funding to expand its artificial intelligence recruitment platform, which uses separate agents for jobseekers and employers to identify potential matches.
The round was led by Air Street Capital, with participation from Creandum and Madrona. It follows the company’s earlier seed financing and takes disclosed funding to $60m.
Jack & Jill operates two conversational AI products. Jack works with candidates to understand their experience, preferences, and career objectives, while Jill works with employers on hiring requirements.
When the two systems identify a mutual fit, the platform can introduce the candidate and company directly, removing the conventional application stage from that particular interaction.
The company says 380,000 people across San Francisco, New York, and London are using Jack, while more than 5,000 businesses are hiring through Jill. Named customers include Corgi, Ramp, Multiverse, Maze, and Attio.
Saaras Mehan, co-founder and chief technology officer, said: “Candidates struggle to be seen, while employers cannot tell who is serious.”
The funding arrives as recruitment is being reshaped by two competing applications of AI. Candidates can use generative tools to produce CVs, covering letters, and applications at much greater scale, while employers are adopting automation to source, screen, and communicate with applicants.
That can increase the volume of activity on both sides without necessarily improving matching. Employers may receive large numbers of polished applications, while candidates can apply to more jobs but still struggle to identify where they have a realistic prospect of progressing.
Jack & Jill is attempting to address that problem by using AI as an intermediary rather than simply an application-generation or screening tool.
The model relies on both sides giving an agent enough information to identify relevant matches before a formal application takes place. That makes the quality of the information captured by each agent central to the product’s usefulness.
The approach also changes some of the economics of recruitment. Traditional job boards generate value through listings, search, and advertising, while recruiters depend on human networks and judgement but are constrained by how many candidates and clients an individual consultant can represent.
An agent-based marketplace aims to combine personalised interaction with software-scale coverage. If successful, the system can search a much larger pool while retaining information about preferences that may not appear in a conventional CV or job description.
That proposition creates governance questions. Recruitment decisions can affect careers and income, making accuracy, bias, data handling, explainability, and human oversight more consequential than in many lower-stakes AI applications.
Employers using automated recruitment tools need to understand what criteria systems apply and whether those criteria unintentionally disadvantage groups of candidates. Applicants likewise need confidence that information disclosed to an agent is being handled appropriately and is not narrowing opportunities through unreliable inference.
The commercial opportunity is substantial because recruitment remains expensive and time-consuming. Businesses pay for internal talent teams, agencies, job advertising, applicant-tracking platforms, assessments, and interviews while still frequently reporting difficulty finding suitable candidates.
AI businesses are consequently competing across almost every stage of the hiring process, including sourcing, scheduling, assessment, interview preparation, skills matching, and workforce planning.
Jack & Jill’s latest financing gives it additional capital to expand while that market is still taking shape. The company is recruiting across London, San Francisco, and New York as it develops the platform and grows its candidate and employer networks.
Its central challenge will be maintaining matching quality as participation increases. Recruitment marketplaces benefit from scale, but poor recommendations, opaque decisions, or weak candidate experiences can quickly erode trust.
The Series A also illustrates continuing investor appetite for AI companies applying models to established commercial workflows rather than concentrating solely on foundation-model development.
Recruitment is a particularly active test case because it contains large volumes of text, repeated communication, search, and matching. It is also one in which the human consequences of automated decisions are immediately visible.
Jack & Jill’s next phase will show whether its agent-led approach can reduce the application overload associated with digital hiring and convert a growing user base into a durable two-sided recruitment marketplace.
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