info@wasabiproject.eu

R2M Solution - Company and Team

Innovation company bridging research and market in AI, AR/VR, and big data.
  • 150+ specialists,
  • 40% women,
  • 25% PhD.

Involved in 100+ EU research projects and with an entire division for B2B and commercialization of products.

Giuseppe Scarpi

Giuseppe Scarpi

Project coordinator

  • MSC in Engineering,
  • Business Development manager in R2M,
  • leads project activities and relationship with WASABI and DIHL.
  • Also responsible for AVANTI (skill assessment SaaS brought to VAFER as background IP).​
Marco Demutti

Marco Demutti

Technical Leader:

  • MSc in Engineering,
  • AI and AR/VR expert,
  • will lead and contribute to software development, model tuning, integration in the WASABI ecosystem​
Jyoti Dhiman

Jyoti Dhiman

Software developer:

  • Sc in Computer Science,
  • Experience in AI, and data science, she will contribute to the development of the tool and organize the validation.​
Michela Zabaglia

Michela Zabaglia

Expert in communication and dissemination:

  • marketing, communication & dissemination specialist,
  • strong knowledge in content production,
  • communication for EU Institutions and projects,
  • worked for EU agencies ETF and EFSA​

EXPERIMENT OVERVIEW

OVERVIEW

Goals: VAFER enhance both the onboarding and on-the-job training processes in high-tech manufacturing

How: innovative AI and voice-powered solutions with integration of OVOS and our proprietary solutions

Onboarding: leverage AI-based voice interviews to discover existing skills and personalize the onboarding

Training on-the-job: voice assistant assist trainees with hints and reminds during hands-on sessions

In general: improve overall trainee satisfaction and performance

CHALLENGES

  • Technological integration of OVOS and other AI technologies to obtain an effective, seamless integration
  • Data privacy with sensitive data like CVs and procedures
  • User adoption to demonstrate concrete advantages and convince of effectiveness of privacy measures
  • Customization and scalability for different industries
  • Real-time performance to ensure accurate feedback without latency is essential for practical training sessions

OBJECTIVES

  • Reduce onboarding by up to 15% through AI-driven skill assessments and voice interviewer to personalize onboarding.
  • Enhance practical training with real-time, voice-activated support on practical sessions, reducing cognitive load, errors.
  • Promote knowledge retention through active learning, with AI-driven interactions to encourage professional growth.
  • Integrate advanced technologies OVOS and other AI technologies for effective onboarding and training solution.

EXPERIMENT IMPACT

EXPECTED RESULTS (KPIs)

KPI Expected value
Reduction in average onboarding time ⇒ Up to 15% compared to the current baseline ⇒
Improvement in onboarding liking ⇒ Up to 20% measured with pre- and post-assessment
Avg error reduction during practical sessions  ⇒Up to 25% compared to the current baseline
Improved training retention ⇒ Up to 15%, measured with mentor assessment

AS-IS SITUATION

USE CASE 1 : onboarding process
Lengthy and inefficient
Past experience of new hires not taken into account
Redundant training, frustrating and boring
Trainees distracted may miss important information

USE CASE 2: support for the on-the-job training
Trainees struggle to remember complex procedures
Frequent errors and a higher cognitive load
Impact on their focus and performance

TO-BE SITUATION

Onboarding more streamlined and effective
Skill Assessment + voice interview with OVOS
Increased trainee satisfaction
Higher engagement, higher attention

Training with real-time, voice-activated support for practical sessions
Trainees focused while getting guidance and feedback
Reduction of errors and enhanced training experience

EXPERIMENT WORK PLAN

WP0: Project Coordination

  • Milestones: IPR plan, Handbook ready, Project completion.​
  • Deliverables: IPR Plan, Experiment Handbook, Final Report.​

WP1: Skill Assessment System Development

  • Milestones: Initial design, Prototype, Final system.​

  • Deliverables: System Specifications, MVP.​

    WP2: Real-Time Training Support System Development

    • Milestones: Initial design, Prototype, Final system.​
    • Deliverables: System Specifications, MVP.​

    WP3: Integration, Testing, and Validation

    • Milestones: Integration completed, Evaluation completed.​
    • Deliverables: Evaluation Report.​

    WP4: Dissemination and Exploitation (support of DIHL)

    • Milestones: Business plan, Final report.​
    • Deliverables: Business Plan, Final Report.​
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