Does it show skill progression, not just a list?

Yes. Talenture estimates proficiency per skill, clusters skills into domains to show genuine expertise, and reads career trajectory and title progression from the candidate's dated work history. You see depth and direction, not a flat list.

What signals end up in a parsed profile?

Each profile carries skills split into hard skills, soft skills, and tooling, with proficiency and recency, plus seniority, geocoded location, languages, workplace values, salary expectations, education, quantified achievements, and a vector embedding for semantic matching.

How accurate is the extraction?

Each profile is built from several parallel extraction passes covering personal details, seniority, education, and dated work history. Skills are categorised into hard skills, soft skills, and tooling, and quantified achievements are pulled directly from the CV text.

Do I still need to screen CVs manually?

No. Once a CV is parsed, the profile flows straight into matching and ranking. You review structured profiles and scores instead of reading raw documents, which removes the data-entry and first-pass screening work.

What happens to skills the candidate listed years ago?

They still count, but less. A half-life recency weighting reduces the score of skills that have not been used recently, so a profile reflects what the candidate can do now, not only what they once did.

Is candidate data handled securely?

Yes. All candidate data is GDPR-compliant, candidates can request deletion at any time, and nothing is shared with third parties. Parsing exists to structure data for matching, not to retain it beyond what the workflow needs.

Stage 2 of the hiring mission · Find and understand candidates

Every CV is a different shape. Reading 200 of them is a week you don't have.

Q: Which file formats and languages are supported?

Talenture parses standard CV formats and handles over 100 languages. Proper nouns stay in the original language while roles and skills are translated to English, so a multilingual pipeline can still be compared on equal terms.

Talenture turns any CV, in 100+ languages, into a rich, comparable profile in seconds: skills with recency, dated history, and 4–8 quantified achievements. Ready to rank the moment it lands.

Works with your ATS: candidates flow in, scorecards flow back.

Every CV becomes a structured profile here, so matching has real data to score, not keywords.

See a sample scorecard Book 15 minutes with the founder

100+: languages parsed
4 to 8: quantified wins per profile
Seconds: from upload to matchable profile

What is AI CV parsing?

AI CV parsing reads an unstructured resume and extracts the facts a recruiter needs: skills with proficiency, seniority, education, dated work history, and quantified wins, organised into consistent fields. Talenture does this in over 100 languages and enriches every profile so it is ready to match and compare instantly.

The profile layer of the candidate scorecard: the rich, matchable record every other layer is built on.

See the full scorecard

app.talenture.ai/candidates/mira-n/profile

Live demo

This is the real product running on sample data. Open any section to explore it.

One CV, read once and turned into a profile you can compare against every other.

Multilingual by design

A CV in Dutch, Spanish, or Mandarin becomes a profile you can compare like any other. Proper nouns stay in the original language, while roles and skills are translated to English for uniform matching.

CV parsing in 100+ languages
Names and companies preserved accurately
Skills normalised for fair comparison

Skill progression, not a flat list

A list of skills tells you what someone has touched. Talenture reads how those skills developed across the candidate's dated roles, so you see depth and direction, not just a tag cloud.

Proficiency estimated per skill, not a yes or no
Skills clustered into domains to show real expertise
Career trajectory read from dated, ordered roles
Title progression captured, for example developer to lead

What the CV never says

Salary, notice period and work preferences, collected up front: the deal-breakers that otherwise surface at the interview.

Achievements, not just job titles

Talenture pulls out quantifiable wins so you see impact at a glance: percentages, currency figures, and concrete outcomes drawn straight from the candidate's own words.

4 to 8 quantified achievements per profile
Hard skills, soft skills, and tooling separated
Action verbs and measurable results highlighted

The profile at work

The same profile scored against one role. The rail walks the whole record: match, evidence, notes, practice interview, recap.

Skills weighted by recency

A skill used last year counts for more than one last touched a decade ago. Every skill is weighted by when it was last used, so current strengths stand out and stale ones fade.

A profile rich enough to match on

By the time parsing finishes, a profile is far more than text. It carries the structured signals the matching engine needs to rank fairly across every dimension.

Location geocoded to real coordinates for commute scoring
Seniority inferred from titles when a CV does not state it
Languages, workplace values, and salary expectations captured
A vector embedding built for semantic matching

How AI candidate profiles work

Three steps from an unstructured CV to a rich, matchable profile.

1
Upload a CV in any language
Talenture reads the candidate CV in over 100 languages, preserving proper nouns while translating roles and skills to English for consistent comparison.
2
Extract the signals that matter
Parallel extraction passes identify personal details, seniority, education, dated work history, skills, and quantified achievements.
3
Enrich the profile for matching
Skills receive proficiency and recency context, while location, seniority, preferences, and a semantic embedding make the profile ready to rank.

Frequently asked questions