Frequently asked questions

What does it cost, and what does a credit buy?

There is a free tier and no credit card is required to start. Beyond it Talentino is pay-as-you-go: AI actions are priced in credits, and you confirm the exact cost of a scoring run before it starts.

Does Talentino support multiple languages?

Yes. Talentino parses resumes in many languages and detects the candidate's language automatically, and outreach emails warn you when languages differ. The interface itself ships in English and French.

Can the whole hiring team work in Talentino?

Yes. A shared talent pool, candidate notes, activity tracking and role-based permissions are built in, so the whole hiring team works from the same evidence.

How accurate is the analysis, and how is it validated?

Every score is built the same way: requirements extracted from your job description, each one graded against the resume, with the supporting lines quoted so you can check the judgment yourself. Talentino is a decision-support tool: the scores and explanations are advisory, and the evidence is on screen for you to check before you decide. And the engine is deterministic by design: the same criteria and the same weighting are applied to every candidate, so what you check is one standard rather than a fresh opinion per resume.

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Is it fully automated, or does the recruiter stay involved?

Talentino does the reading. It processes the pile and produces a first-pass analysis with the explanation attached, and the scores and rankings it produces are advisory. Shortlist and reject decisions are applied from the candidate review screen with the cited evidence in view, and each status keeps a record of who applied it.

Can we customize scoring criteria?

Yes. Adjust the weighting for skills, experience and qualifications (emphasize soft skills for managers, technical depth for engineers) and define must-have versus nice-to-have criteria, down to hard deal-breakers.

How does Talentino address fairness and bias?

Three ways: evaluation reads the skills and experience the resume documents, extracted the same way for every candidate; the same criteria are applied uniformly to every candidate for a role; and every score carries an explanation you can review and challenge.

How is candidate data secured?

Resume files are encrypted in transit and at rest. Every record carries the customer account it belongs to, and every request is scoped to that account. Access is governed by role-based permissions, and safeguards against manipulation attempts like keyword stuffing are built in.

Where does the data run, and where does the AI processing happen?

The application and file storage run on AWS in Frankfurt (eu-central-1). Those two components are named deliberately: model inference runs on third-party AI providers, and we do not extend the Frankfurt statement to cover them.

What can we put in front of someone who challenges a decision?

In the app: the role's weighted criteria, per-requirement grades with citations from the resume, the score and the weighting behind it, and the audit trail. Each status keeps a record of who applied it, and because the criteria and the weighting are fixed on the role and stored with it, a past decision can be re-run and checked against the same standard. The PDF export is narrower: every requirement scored out of 100 and grouped by how well it was met, each with the scoring explanation, deal-breakers flagged, and the overall score with its summary, strengths and concerns. The citations, the weighting and the status history stay in the app.

We have to review the data-protection paperwork before we can sign. Where do we start?

Start with the privacy policy and the security page: they carry where the data runs, how candidate files are handled, and what you can export if a decision is challenged. Anything they do not cover, ask us on a demo call.

Read the privacy policyWhere the data runs