Let candidates experience the role before you hire them.

An AI job tryout is a simulation of the role a candidate is applying for. Rather than answering questions about their experience, the candidate is placed in a realistic work situation and has to handle it while an AI persona plays the other party.

The improvement over a traditional interview is one of evidence. An interview produces a candidate's account of how they work; a tryout produces a record of how they actually worked, in a situation you designed. Those are different kinds of information, and the second is closer to what you are trying to predict.

It is particularly useful for customer-facing roles, where the qualities that matter — patience, judgment under pressure, the ability to hold a position without escalating a conversation — are almost impossible to assess from a CV and easy to overstate in an interview.

Practically, it also means candidates who interview poorly but work well stop being filtered out early. That group is larger than most hiring teams assume, and it disproportionately includes people who are less rehearsed, less coached, or less confident in a second language.

The persona is a dynamic character rather than a recorded prompt. It has a role, a temperament and a goal in the conversation, and it responds to what the candidate says as the exchange develops.

In a sales scenario, the persona might present as a sceptical prospect with a specific objection. If the candidate handles that objection convincingly, the persona moves on to a harder one. If the candidate deflects, the persona presses. The path through the conversation is different for every candidate, because it is determined by their own responses.

This matters for two reasons. It produces a more realistic assessment, since real customers do not read from a script either. And it makes the tryout substantially harder to prepare for or game — a candidate who has heard about the scenario from a friend still has to handle a conversation that unfolds according to what they themselves say.

Behind the interaction, Talvin evaluates the transcript against the criteria you set for that role and returns a graded report with the reasoning attached.

The clearest fit is any role where the job is largely conversation: sales, customer support, hospitality, front-of-house, account management, cabin crew, service desks and client-facing consulting.

In those roles, technical qualification is often a low bar and the real differentiator is behavioural — how someone handles a complaint, whether they can disagree with a customer without losing them, whether they stay clear when the other person is not.

Job Tryouts also work well for roles that involve explaining something complex to someone who is resistant to hearing it. That covers more technical positions than people expect: a solutions engineer defending an architectural decision, or a support specialist walking an angry user through a workaround.

They are less useful where the work is solitary and the output is a deliverable rather than an interaction. For those roles, a conventional AI interview with deep technical probing usually tells you more.

The honest answer is that simulation-based assessment has a strong track record in occupational psychology generally, and that Talvin's implementation is new enough that we would rather explain the reasoning than quote a number we cannot yet stand behind.

The reasoning is this. Every assessment method is a proxy for future performance, and proxies vary in how much interpretation sits between the evidence and the conclusion. A CV requires you to trust a self-report. An interview requires you to trust a recalled account, filtered through how well someone presents. A simulation requires you to interpret an observed behaviour in a situation close to the real one — fewer layers, less inference.

What we would encourage is testing it against your own bar rather than ours. Run a tryout alongside your existing process for one role, compare the two shortlists, and see whether the candidates it surfaces are people your hiring managers would have wanted to meet.

That is a more useful validation than any figure we could publish, because it is measured against your roles and your definition of a good hire.

Better than most hiring teams expect, for a reason worth understanding.

The common assumption is that candidates dislike being assessed by AI. What candidates actually dislike is being rejected without explanation, waiting weeks for a response, or feeling that a decision was made on something other than their ability. A tryout addresses all three: it is quick, it is obviously related to the job, and it gives them a chance to demonstrate something a CV cannot show.

Candidates who are strong at the work but weak at interviews tend to prefer it outright. So do people who are less rehearsed or who are working in a second language, because the assessment is about how they handle the situation rather than how polished their prepared answers are.

The framing matters. Candidates told clearly what the tryout involves and why it is being used respond well. Candidates who encounter it unexplained, mid-process, are more likely to disengage.

A skills test asks whether someone can complete a task. A job tryout asks how they behave while doing it.

For a lot of technical hiring, a skills test is the right tool — if you need to know whether someone can write working SQL, have them write SQL. It is objective, quick to grade, and hard to argue with.

What a skills test cannot show you is what happens when the task involves another person. Whether a candidate can de-escalate an angry customer, hold a price under pressure, or explain a technical constraint to someone who does not want to hear it are not tasks with a correct output — they are interactions with better and worse handling.

That is the gap Job Tryouts fill. Many teams run both: a skills test as a technical gate, and a tryout for the behavioural half of the role.

Yes, and most customers do after the first few tryouts.

You can begin from a role template and adjust it, or write a scenario from scratch. The elements you control are the situation itself, the persona's role and temperament, how difficult they are, and the criteria the response is graded against.

The scenarios that work best are usually the ones taken directly from a real incident — the complaint that comes up every month, the objection your sales team loses deals to, the conversation new hires consistently find hardest in their first quarter. Those produce sharper signal than a generic difficult-customer scenario, because they discriminate on exactly the thing your role requires.

If you are unsure where to start, it is usually worth asking the hiring manager which conversation a new starter is most likely to get wrong. That is the tryout worth building.

Most run between five and fifteen minutes, depending on the complexity of the scenario and how far the conversation develops.

That is deliberately short. A tryout is a screening instrument, not a work trial — it exists to tell you whether a candidate is worth a real interview, and asking for an hour of unpaid effort at that stage damages completion rates and your employer brand.

Candidates complete it in their own time, by voice, with nothing to install and no scheduling involved.

By shifting the evaluation from impression to demonstrated behaviour.

Traditional hiring is measurably influenced by factors with little bearing on performance — name, accent, appearance, educational background, and how similar a candidate seems to the person interviewing them. Those signals are hardest to suppress precisely when a decision is subjective.

A tryout narrows what is being judged. Every candidate faces the same scenario, the same persona and the same grading criteria, and the score traces back to what they actually said and did.

The honest caveat is the same one that applies to any AI assessment: no system is automatically unbiased, and any vendor claiming otherwise is overselling. What makes an approach defensible is whether the criteria are genuinely role-relevant, whether the scoring can be audited, and whether a human remains accountable for the decision. Job Tryouts are built to be checked rather than trusted.

Job Tryouts sit inside the process you already run rather than replacing a stage of it.

The usual pattern is to add a tryout after the initial AI interview and before the hiring manager round — so the people who reach a human have already demonstrated the behaviour the role depends on. Some teams instead use it as the first step for high-volume frontline roles, where behaviour matters more than background.

There is no implementation project. Scenarios are configured in the platform, invitations go out automatically, and results return to your ATS alongside the rest of the candidate record.

The lowest-risk way to start is to run it in parallel on one live role — keep your existing process untouched, add a tryout alongside it, and compare the two shortlists before changing anything.

Job Tryouts are included in every Talvin plan. There is no separate licence or add-on fee.

Usage draws from the same monthly allowance as AI interviews, billed as interview time, so a tryout consumes minutes in the same way a screening conversation does. Plans start at $175 a month. See full pricing →