Turn skills data into decisions that actually work

Move beyond degrees. See real capabilities. Build workforces that perform. We transform fragmented skills data into a clear, task- and proficiency-driven layer for hiring, workforce planning, training, and policy—powered by GenAI and validated by experts.

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Task-based Proficiency-driven Taxonomy-compatible Employer-ready Expert-validated

The problem we solve

Most organisations don’t struggle with data—they struggle with turning skills data into action. The result: slower hiring, underused talent, training that misses operational needs, and career guidance that feels abstract. A Skills-First approach closes this gap by focusing on what people can actually do, and translating skills into tasks, proficiency expectations, and outcomes you can use.

Faster, more effective recruitment

Hiring still relies heavily on degrees, job titles, and vague requirements—making screening slow, inconsistent, and biased toward proxies rather than capability.

  • Reduce time-to-hire with skills-based pre-screening
  • Improve match quality by comparing real capabilities
  • Increase fairness by lowering overreliance on credentials

Unleash the hidden gems in your talent pool

Valuable skills often remain invisible inside CVs, HR systems, and teams. Without a common language, internal mobility and succession planning depend on guesswork.

  • Surface transferable skills across roles and teams
  • Strengthen internal mobility and succession pipelines
  • Re-engage past candidates and talent pools faster

Training tailored to real work

Generic training rarely maps to what people must do on the job. Organisations struggle to define “job-ready” and to target upskilling where it creates measurable impact.

  • Align training to tasks and proficiency levels
  • Reduce waste and improve learning ROI
  • Support modular training and micro-credentials

Career orientation & lifelong learning

People often lack visibility on what skills matter, which roles they could grow into, and what learning path gets them there. This limits mobility, employability, and long-term wellbeing.

  • Provide practical guidance grounded in real jobs
  • Recommend gap-based upskilling pathways
  • Support workforce participation and sustainable employment

Our solution: Skills-First, done right

Most initiatives stop at classification. We go further by adding an operational layer that turns skills into something people can use: real tasks, clear proficiency expectations, and learning outcomes mapped back to jobs.

  • Build on what you already use — taxonomies, job families, role profiles, competency models.
  • Add a task + proficiency layer — human-readable, assessable, and comparable.
  • Keep traceability — link tasks back to underlying skills and sources.

Who it’s built for

Designed to deliver value across the labour-market ecosystem, with direct employer applicability.

Employers & Companies

  • Define roles with clarity and precision
  • Recruit based on skills, not proxies
  • Unlock internal mobility and succession pipelines
  • Reduce time-to-hire and training waste

Governments & Public Authorities

  • Design smarter active labour-market policies
  • Target reskilling and upskilling where it matters
  • Support mobility, regional planning, and integration
  • Measure impact with skills-based evidence

Education & Training Providers

  • Align curricula with real job requirements
  • Co-create training with employers
  • Deliver modular learning and micro-credentials
  • Address concrete skill gaps quickly

Jobseekers

  • Build stronger skills profiles
  • Receive realistic career guidance
  • Access targeted upskilling pathways
  • Improve employability and outcomes

How it works

1

Define skills

Consolidate skills and knowledge using existing frameworks and labour-market text data (vacancies, role profiles).

2

Group into tasks

Cluster granular skills into meaningful work activities that stakeholders recognise and can discuss.

3

Assign proficiency

Define observable expectations (e.g. junior → expert) with criteria and potential assessment methods.

4

Derive learning outcomes

Create structured outcomes aligned with labour-market needs and traceable to tasks and skills.

5

Generate training foundations

Produce draft training structures to accelerate course design, micro-credentials, and upskilling pathways—then refine with instructional design.

GenAI, with control

GenAI is the engine of the approach—accelerating analysis, structuring, and drafting at scale. Outputs are always treated as proposals and validated by subject-matter and industry experts to ensure quality, relevance, and contextual accuracy.

What GenAI accelerates

  • Extracting skills from unstructured text
  • Clustering skills into coherent tasks
  • Drafting proficiency criteria and learning outcomes
  • Producing training foundations for review

What experts own

  • Validation and contextual refinement
  • Quality assurance and governance
  • Industry relevance and role realism
  • Final sign-off on outputs

The business impact

A shared, task-based and proficiency-driven skills layer improves interoperability, transparency, and mobility—enabling better matching, more targeted training, and a workforce ready for future transformation.

Operational outcomes

  • Faster, skills-based screening and matching
  • Clearer gaps and more targeted upskilling
  • Reduced training waste and better ROI
  • Improved internal mobility and succession

Ecosystem outcomes

  • Shared language across stakeholders
  • Better alignment between jobs and education
  • More portable skills and clearer pathways
  • Stronger evidence for policy decisions

Ready to move beyond qualifications?

Let’s discuss your use case and what a pilot could look like. This page is intentionally lightweight—replace the links below with your preferred contact method.

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