Build the Marketplace Where Nobody Has to Search

01 /The Concept

Founded just this year, with a team of only 12 people – and yet Jack & Jill has already raised its first investment round: $20M straight out of the gate.

Jack & Jill claims to have built a new kind of job marketplace – one fundamentally unlike traditional job sites with their mountains of resumes and listings that candidates and employers must manually sift through.

In the pitch deck that secured this round, Jack & Jill argues that such marketplaces will become their own recognized category within the next 12 months – and that Jack & Jill will be the winner in that category.

Jack & Jill are two AI agents – one named Jack, one named Jill.

Jack works with candidates, helping them not miss good roles at good companies. With 10,000 new job postings appearing every hour, no human can manually find and evaluate every role they might be right for. So Jack does the searching and matching for them.

To get started, a candidate has a 20-minute voice conversation with Jack – who learns their skills, preferences, and career goals. After that, Jack starts surfacing relevant opportunities and sending them to the candidate by email.

If a candidate likes a role, they hit a button – and Jack connects them directly with the recruiter handling that position.

Candidates can also talk with Jack while still in the "figuring out what's next" phase – just by describing their current role, compensation, and what they want. In that mode, Jack acts as a career mentor: assessing current skills, benchmarking salary, and mapping out steps toward the next level.

Naturally, those conversations help Jack develop a deeper picture of the candidate – which over time produces more precise job matching.

For candidates, all of this is free.

Jill works with companies. Through Jill, companies can find better-fit candidates faster and more affordably than through traditional recruiting agencies.

The process starts with Jill having a conversation with at least the recruiter – ideally also the hiring manager. This allows Jill to understand requirements and company culture at a depth that never comes through in a standard job description full of clichés like "dynamic team" and "fast-paced environment."

After that, Jill communicates with Jack – who knows a large pool of candidates in similarly real depth, far beyond what any resume conveys with its own clichés like "self-starter," "results-oriented," and "strategic thinker."

That "communication" is, of course, algorithmic matching – comparing the profiles Jill has built of companies and roles with the candidate profiles Jack has assembled. When a strong match is found, the candidate's profile goes to the recruiter, who also hits a button to connect – but in practice, connections only happen when both sides have flagged each other. It's essentially mutual opt-in, like a hiring version of Tinder.

Jill also follows up with companies after they hire a recommended candidate – asking how that person is performing. This feedback loop helps Jill continuously improve the matching algorithm.

Companies pay only on a successful hire – 10% of the new employee's base salary.

Jack & Jill launched in London and spread quickly from there. Between April and August, registered candidates grew from a few thousand to nearly 35,000. The platform now has more than 52,000 registered candidates.

02 /Why It Matters

Jack & Jill is claiming to have invented a new type of marketplace – essentially a "marketplace without a marketplace," where you never search because it surfaces the best options for you. This maps almost perfectly onto the concept of an "ideal system" in TRIZ theory: an ideal system is one that doesn't exist, but all of its functions get performed. That's exactly what's happening here.

The startup also predicts that job search platforms like this will multiply fast enough to constitute their own category within 12 months. That's already happening.

Dex ([related review](/review/ty-mozhesh-sdelat-jetu-skazku-bylju-na-milliardy-dollarov)), also founded this year, raised $3.1M in April while still in closed beta – focusing specifically on tech professionals.

Mercor ([covered here](/review/a-ty-jetu-ofigennuju-vozmozhnost-mozhesh-razgljadet)) went even narrower, applying the same model to AI specialist hiring. Yet despite that focus, it began growing sharply – at 51% month-over-month last fall. By February, it was tracking $75M in annualized revenue and raised $100M at a $2B valuation. Now Mercor is in talks for a new round – reportedly at a $10B valuation – with annualized revenue already at $450M.

Mercor's more ambitious goal: predict how well candidates will actually perform after joining a company, making its matching algorithm strategically indispensable rather than just convenient.

Laborup ([related review](/review/starye-rabotnye-sajty-pora-vykidyvat-na-pomojku-istorii)) picked a completely different niche – industrial workers. Founded last year, it's raised $7.7M total, with $5.8M of that coming in August. It launched a pilot program in a single city earlier this year, and within three months captured 20% of all workers in that city as users.

Manufacturers love it because, as one client put it: "Laborup doesn't just find workers – it finds the right workers." By every criterion: from required skills to willingness to work at that specific company – even whether the commute from home is manageable.

Also worth mentioning is Boardy ([covered here](/review/produkt-kotoryj-sam-prinosit-investorov)), which raised $11M on a professional network for entrepreneurs, founders, experts, and investors built on the same voice AI agent technology.

Boardy's AI calls each new member, learns what they're about and what they want – then proposes introductions to other members it has spoken with. It sends both sides the other's profile, and only makes the introduction if both agree.

03 /Opportunities

The broad trend is building "marketplaces without marketplaces" – platforms where you never have to search.

Because AI can find the right matches far faster than any human. And far more accurately – since it can have real conversations with both sides that surface many more preferences and constraints than could ever fit in a posted job listing.

Funnily enough, Boardy fits the same trend, because a professional network is also a marketplace of connections – one where people search for others by a large number of hard-to-formalize criteria.

Which immediately suggests that this concept is applicable in far more domains than just job search and hiring – and that breadth may be exactly where the next startup opportunity lives.

Job search is attractive because the market is enormous and the need is perpetual. But competition is already getting fierce. In what other sector – even a narrow niche – would you want to build a marketplace on this principle? There are actually quite a few options if you think it through.