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Use case · Education and Training

More time with learners. Less time with forms.

A vocational training body opens a new application round: hundreds of files, detailed regulations and tight deadlines. See how AI, connected to the existing platforms, checks eligibility, builds timetables and summarises what learners think. The same pattern applies to nurseries and pre-schools: enrolments, fees and communication with families.

Tiago

Coordinator · Vocational training

Eligibility checks are rigorous and repetitive. I would rather spend that time supporting people who are in training.

Illustrative persona, based on typical training management roles.

An application round

Meet Tiago.

Tiago coordinates vocational training at a regional public body. Every new programme brings a regulation, a platform and hundreds of applications to review.

  1. The round opens. In ten days, 320 applications arrive, each with 6 to 12 documents.

  2. The team checks age, qualifications, employment status and missing documents, one by one.

  3. With classes defined, the timetable puzzle begins: trainers, rooms and availability.

  4. 400 open-ended survey answers arrive. Nobody has time to read them all.

The challenge

Clear rules, manual work.

Eligibility is written in the regulation. The data is on the platform. What is missing is someone to connect the two — and that takes weeks of specialist time.

25 min

per application spent on eligibility checks

3 wks

to finalise timetables for a new round

10%

of open survey answers ever get read

Estimate Illustrative figures based on typical scenarios. Every assessment establishes your organisation's actual numbers.

  • Scattered criteria

    Regulations, ordinances and technical guidance in different documents, updated frequently.

  • Hard timetables

    Dozens of constraints: shared trainers, rooms, transport, evening classes.

  • Unheard feedback

    What learners write in surveys rarely reaches the people who decide.

What changes

AI reads the regulation and the application — and shows why.

With MCP servers connected to the application platform, the school management system and the regulations repository, the assistant pre-screens and justifies every finding. The final decision belongs to the team.

Today

  • Each criterion checked by hand
  • Document requests sent one at a time
  • Timetables built in spreadsheets
  • Surveys summarised only as numbers
  • Families waiting for answers about enrolments and fees

With AI connected through MCP

  • Eligibility pre-screening with justification
  • Document requests grouped and approved
  • Timetable proposals that respect every constraint
  • Themes and suggestions drawn from open answers
  • Replies to families prepared from the child's record, for approval

Guided demonstrations

Two tasks that no longer take weeks.

Choose a demonstration. Decisions about applicants and timetables always remain with the team.

Application pre-screening — Vocational training (simulation)

Processed in the EU

Tiago has 320 applications to review by Friday.

    MCP calls

    0
      • Applications
      • Regulations

      Simulation with fictitious data. No real system is contacted.

      Connected systems

      The MCP servers behind this solution.

      Read connections to the existing platforms; changes (document requests, timetable publication) require approval.

      • Application platform

        Application portals and programme management

        • applications.listRead
        • applications.documentsRead
        • applications.request_documentsWrite with approval
      • Regulations

        Regulations, ordinances and technical guidance

        • regulations.criteriaRead
        • regulations.searchRead
      • School and training management

        Classes, trainers, rooms and session records

        • classes.listRead
        • trainers.availabilityRead
        • timetables.publishWrite with approval
      • Surveys

        Satisfaction survey platforms

        • surveys.responsesRead
        • surveys.themesRead

      Applicant and learner data is processed in the European Union, through our partners, with personal-data minimisation.

      Results

      Weeks of work given back to the team.

      Estimates for a round of around 300 applications and 12 classes.

      • Eligibility review per application

        Before: ≈ 25 min After: ≈ 7 min

      • Timetable proposal for the round

        Before: ≈ 3 weeks After: ≈ 2 days

      • Open answers analysed

        Before: ≈ 10% After: 100%

      Estimate Illustrative figures based on typical scenarios. Every assessment establishes your organisation's actual numbers.

      Safeguards

      Fair, explainable and auditable.

      • Explainability

        Every eligibility finding states the criterion and the document it is based on.

      • Human decisions

        AI pre-screens; admissions, exclusions and timetables are decided by the team.

      • Data protection

        Access only to the data needed, with pseudonymisation wherever possible.

      • Processing in the EU

        Applicant and learner data processed inside the European Union.

      Frequently asked questions

      Does the AI decide who is admitted?

      No. The AI prepares a pre-screening with the justification for each criterion. The decision always belongs to the technical team, who can confirm or correct it.

      What happens when the regulation changes?

      Criteria are read directly from the regulations repository through MCP. When the document is updated, the analysis uses the new version — with no reprogramming.

      Will it work with our school management platform?

      In most cases, yes. We connect through APIs, databases or exports. The initial assessment confirms the best approach.

      Accessibility

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