New: TechWolf is named in the Gartner® report on the 5 Most Common Uses of AI in HR.

Frequently Asked Questions

How can we help?

Short answers about the product, the data it uses, and how a rollout works. Each one stands on its own, so you can read a single answer and stop there.

Skills AI and Methodology

How many skills should be on a job profile?

Around 30 to 35 skills, with 10 to 15 flagged as critical. Criticality is decided by how many of a role's tasks need the skill, not by opinion.

Can we bring our own skills taxonomy?

Yes. We typically map 70 to 80% of an existing skill list onto our ontology out of the box, and the hierarchy above it stays yours to govern.

What proficiency scale do you use, and can we keep the one we have?

Five levels by default, configurable from three to eight or mapped onto your existing HRIS scale, with job and employee proficiency running on separate tracks.

What is job architecture, and why are companies redesigning it?

Job architecture is the job families, profiles and levels underneath your roles. Most were built for compensation, not skills, which is why they are being redesigned.

What is a skills ontology, and how is it different from a taxonomy?

Vocabulary, ontology and taxonomy are three separate layers. The ontology holds the relationships between skills, and it is the layer most often skipped.

What is workforce analytics, and is it the same as skills intelligence?

Workforce analytics reports backwards on HR system data; skills intelligence infers what people can actually do today. Most enterprises need both.

What is workforce intelligence, and how is it different from work intelligence?

Workforce intelligence is the market's umbrella term for four separate disciplines, and work intelligence is the one that starts from the work rather than the people.

What's the difference between skills intelligence and talent intelligence?

Talent intelligence is the decision layer for hiring, mobility and planning, and skills intelligence is the data layer it runs on.

Data Sources and Integrations

Why are our Workday Skills Cloud profiles empty, and how do we fill them?

Skills Cloud stores skills but does not infer them. TechWolf infers them from systems you already run and writes them back into Workday as the system of record.

Does TechWolf use LinkedIn or other external profile data?

No. TechWolf infers skills only from data your organization owns: profile data is self-reported, creates GDPR exposure, and scraping breaches LinkedIn's terms.

Does completed learning content count as skill evidence?

Yes, when the course description comes with the completion, because our models infer skills from that text. Completions count; enrollments and in-progress courses do not.

How does the technical data exchange work?

Data reaches TechWolf by direct API or by file transfer over SFTP or S3, with full loads by default and a sync cadence you set: daily, weekly or monthly.

Which systems can TechWolf pull data from?

TechWolf connects to three groups: HR systems such as Workday and SAP SuccessFactors, work systems such as Jira, ServiceNow and Teams, and learning platforms.

Implementation and Timeline

How long does it take to implement a skills intelligence platform, and what does rollout look like?

Three to six months to validated skills on every job and employee. Two to six weeks of that is technical; the rest is validation and change management.

Product and Features

Who validates the AI's suggestions, the employee or their manager?

The employee, by default, through a skill assistant in Teams or Slack. A stricter manager-first route is available as a governance choice you make at rollout.