Skill profile software that keeps every profile current
How it works
How skill profiles get built
Upload data or connect existing systems
Skill extraction runs on CVs, job descriptions, training records and internal documents.
From that input, talentguide identifies the atomic units of capability behind each role or person. Operating a specific machine. Engraving. Working to tolerance. Reading French documentation.
Skill extraction
Those atomic units group into skills.
Skills map to roles. You and your employees start with structured profiles instead of empty templates.
Each competency has a trace to the source
Easy skill and data validation
Full validation fails because nobody has an hour to review 200 competency entries. Skill profiles in talentguide surface only the entries that are high impact and low confidence. High confidence data gets accepted automatically. Irrelevant signals stay hidden.
Automatic skill profile updates
Every evaluation, training completion and certificate renewal writes back to the profile. Expired competencies drop out of role eligibility on their own. Better data produces better matching, and better matching produces better development plans.
Talentguide AI
AI suggests. You decide.
AI does the heavy lifting: reading unstructured data, extracting skills, suggesting proficiency levels, surfacing gaps, proposing coaches and matching learning. But the decisions stay with the people accountable for them.
Every recommendation is a starting point for a human decision, never a replacement for one. That is also what keeps your skills decisions explainable and auditable, which matters more each year as the EU AI Act raises the bar for how automated systems are used in HR.
Transparancy
Trace every skill to evidence.
A skills profile is only as trustworthy as the evidence behind it. Every skill and proficiency level traces back to a source: a document, an evaluation, or a human validation.
Confidence is explicit. Where the evidence is thin, the data is marked low-confidence rather than presented as fact. That honesty is deliberate. Treating every data point as equally certain is how skills tools lose the trust of the people who have to use them, especially technical teams who will not accept an assessment they cannot interrogate. When you can see where a skill came from and how confident the system is, the decision you make on top of it holds up.
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Free trial for up to 5 employees