Matching Intelligence
MAVI's matching intelligence is the set of AI models that power how we pair finance professionals with the companies that need them. It's the decision-making core of our finance talent engine – the part that evaluates a candidate against a role on the dimensions that actually predict success, and returns a match built on data rather than guesswork.
What is MAVI's matching intelligence?
Most recruiting matches on the obvious surface signals: job titles, years of experience, a few keywords on a résumé. That approach is fast but shallow, and it misses the things that actually determine whether someone will thrive in a specific role. Our matching intelligence goes deeper.
It's the layer of our finance talent engine that evaluates candidates on the dimensions that predict real fit – technical skills, systems experience, seniority, and the judgment a role requires – and weighs them against what a specific company actually needs. The result isn't a list of people who look plausible on paper; it's a short list of people who are genuinely likely to succeed.
How does our matching intelligence work?
Our matching intelligence runs on seven distinct intelligence models, trained across hundreds of thousands of finance professionals and validated by human experts. Each model evaluates a different facet of fit, and together they build a far richer picture of a candidate than any single résumé screen could.
The payoff is precision. Instead of handing a client fifty résumés to sift through, we share two profiles per role – and more than nine times out of ten, one of them is the right hire. That level of accuracy is only possible because the matching is driven by models built specifically for finance, not a generic recruiting algorithm.
Why does matching intelligence matter?
Because a bad match is expensive in ways that go beyond salary. A hire who looks right on paper but can't actually do the work costs time, rework, and often a second search – while the role sits unfilled and the team absorbs the gap. Precision at the matching stage is what prevents all of that downstream cost.
It's also what makes global sourcing viable at quality. Evaluating a worldwide pool of talent by hand is impossible; there's simply too much of it. Matching intelligence is what lets us assess that pool at depth and speed, which is why we can promise both quality and a five-day timeline instead of trading one for the other.
How does this find AI-proficient talent?
Our mission is to build the world's largest network of AI-proficient finance talent – professionals who bring finance fundamentals and the fluency to direct AI tools well. Identifying those people is itself a hard evaluation problem, since AI fluency doesn't show up neatly on a résumé, and it's exactly the kind of nuanced fit our matching intelligence is built to assess.
There's a fitting symmetry to it: AI-driven matching, validated by human experts, surfacing the finance professionals who are themselves fluent in pairing judgment with AI. That's the layer modern finance teams need most, and our matching intelligence is what finds it.
How is this different from a keyword search?
A keyword search asks a narrow question: does this résumé contain the right words? It's easy to game, easy to miss strong candidates who describe their experience differently, and blind to everything that isn't written down. It's a filter, not an evaluation.
Our matching intelligence asks a better question: is this person actually right for this role? It weighs skills, experience, and fit in context, the way an expert recruiter would – but across a global pool, at a scale and speed no human team could match. That's the difference between screening résumés and genuinely matching people to roles, and it's the difference at the heart of how MAVI works.