How Logi-Technic screens technicians with AI | Ringtime customer story

Company & context (Logi-Technic)

• Logi-Technic is a technical service provider focused on outsourcing technical profiles in industry (and also field services), including:
  • Electricians
  • Mechanics
  • Electromechanical engineers
  • Plus work planners / maintenance engineering support

Outsourcing model

• Logi-Technic is the employer of the technician (employership).
• Technicians are deployed to customer sites, but remain Logi-Technic employees.
• Positioned intentionally as different from temporary staffing / agency work:
  • Goal is a long-term partnership with both technicians and customers.

Leadership roles

• COO (Lies) focuses on:
  • Operational execution (including “putting out fires”)
  • Process improvement + long-term vision

Example of an operational “fire”:

• A technician incident causing production damage at a customer (e.g., a food company power supply/material loss), requiring both:
  • Technical resolution, and
  • Commercial responsibility alignment

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Talent strategy & recruitment process (before Ringtime)

Recruitment flow (phased)

• Recruitment was described as phases with a strong emphasis on speed due to scarcity:
  • Phase E: candidate gets to Logi-Technic
  • Then subsequent phases: Phase 1 → Phase 2 → Phase 3 (expectations managed via dashboards)

How candidates arrive (Phase E)

Candidates come via:

• Active initiative: technicians applying
• Passive job seekers: outreach on social channels (Facebook / LinkedIn)

Steps in the process

1. Initial administrative screening
   • CV/background checks and basic fit
2. Apply 4 knockout criteria (basic requirements distilled to what blocks or allows progression):
   • Driver’s license
   • Access to the “white van / flexible deployment” requirement (implied mobility)
   • Language ability: Dutch / French / English
   • Location constraint: living in/near Belgium
3. Technical experience is described as essential to proceed

Scarcity & employer-brand requirements

• Technicians are described as “incredibly scarce.”
• Logi-Technic must:
  • Convince people to choose them (not only interview them)
  • Stay attractive as an employer (trust comes with the feet… goes with the horse)
  • Leverage referrals/network effects (e.g., “acquaintances… are best hires”)

Recruitment resourcing & speed targets

• A recruiter team plus a technical recruiter evaluate skills.
• Speed is emphasized throughout:
  • Recruiters contact applicants quickly, but only during office hours
  • After initial screening, they can make a salary offer immediately during the technical recruiter conversation

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Key hiring bottleneck problem (trigger for Ringtime)

• The team noticed candidates applied outside office hours (weekends/evenings).
• But recruiters were not available to respond instantly.
• This produced a measurable drop-off:
  • ~20% of applicants cannot be contacted afterward

Reasoning:

• Without responsiveness (“strike while the iron is hot”), candidates may proceed elsewhere while waiting.

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AI screening solution with Ringtime (WhatsApp + voice)

Ringtime was introduced to:

• Reduce recruiter time spent on unqualified candidates
• Restore 24/7 responsiveness
• Improve throughput without losing relationship quality

Channel strategy: “first WhatsApp, then voice”

1) First touchpoint: WhatsApp (24/7)

• Instant response after application
• Built for clear yes/no knockout questions (e.g., license: yes/no)

2) Second phase: AI telephony bot

• For nuanced questions that aren’t strictly binary
• Examples:
  • Preferred work regime (shift / night / day / weekend)
  • When can you start (e.g., someone preferring January 2027 instead of next week)

Competency-based matching (“bucket system”)

• Logi-Technic does not recruit strictly for narrow vacancies.
• It identifies competency “buckets” that can span skills such as:
  • Electrical, mechanical
  • PLC, pneumatics, hydraulics
  • Motors involvement
  • Welding (example mentioned)
• The AI + phone flow aims to place candidates into the right competency buckets so technical recruiters can follow up effectively.

Human experience preserved

• AI handles qualification; recruiters focus on deeper human-to-human conversations and technology discussion.
• Explicit concern: avoid losing the “warm feeling” of a human-first contact.

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What the AI eliminates / reduces

• Recruiters currently spend time on applicants who turn out not to be viable for client needs.
• With Ringtime:
  • Unqualified candidates are filtered earlier
  • Qualified candidates receive recruiter attention instead of “tick-box” interview throughput

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Frameworks / process playbooks mentioned (implicit)

Phased funnel (managed via dashboards)

• Phase E → Phase 1 → Phase 2 → Phase 3
• Expectations tracked via dashboards

Knockout criteria playbook

• 4 explicit administrative fit requirements before progression

Channel-to-question mapping

• WhatsApp: binary yes/no qualifying
• Voice bot: preference/constraint discovery (work regime, start date)

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Metrics / KPIs and targets (explicit + implied)

Explicit KPI

• 20% applicant drop-out without contact (can’t reach them afterward)

Implied operational KPIs (dashboard-driven)

• Speed of candidate progress across phases:
  • time-to-contact
  • time-to-decision
  • time-to-offer
• Candidate experience metrics:
  • How candidates respond to AI touchpoints

Success criteria (no specific numeric targets stated)

• Make “first contact to start” feasible
• Increase recruiter time spent on qualified matching, not volume handling

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Concrete implementation details & rollout timeline

• Ringtime expected to go live “next week.”
• Initial focus:
  • Ongoing testing and tuning of bot questions (especially where candidates drop out)
  • Refining what counts as a knockout point and improving conversational prompts

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Risks & how they’ll be managed (leadership concerns)

Main concern: candidate readiness

• Whether candidates will drop out because a robot calls instead of a human

Operational handoff concern

• Even if candidates are contacted quickly by AI, they may go silent over weekends.
• Recruiters may re-enter later—requiring ongoing candidate responsiveness.
• Plan: ensure Logi-Technic people intervene as the process progresses.

Additional curiosity

• What “funny” or unexpected answers candidates might provide to the bot

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Expected business impact (1 month and medium term)

Near term (~1 month)

• Run a limited number of candidates through the system while improving question sets
• Recruiters gain capacity to:
  • Match qualified candidates to suitable work regimes/projects
  • Engage latent job seekers (not only spontaneous applicants)
• Application processing shifts toward more marketing/sourcing-style engagement (making candidates “warmer”)

Marketing/sales funnel expansion

• AI qualification increases capacity to handle more candidates from marketing campaigns, since unqualified volume is filtered earlier.

Medium-term expansion vision

• If successful, extend voice agents to:
  • Additional internal processes
  • Possibly even the figures process (internal operations calling)
  • Internal employee calls that repeat the same questions

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Key actionable recommendations embedded in the story

• Respond instantly (24/7) to applications; do not rely on office-hours-only recruitment.
• Separate qualification from relationship-building:
  • AI filters by knockout criteria
  • Humans handle nuanced evaluation and candidate experience
• Use channel fit:
  • WhatsApp for binary qualification
  • Voice for preference/constraint discovery (work regime, start date)
• Implement a competency bucket model to improve customer matching.
• Use dashboards to:
  • Detect slow points (especially outside-office-hour conversion)
  • Accelerate transitions between phases

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Presenters / sources

• Lies — COO, Logi-Technic
• Diederik — interviewer (Ringtime customer story context)
• Rus Dumarij — CEO, Logi-Technic
• Ringtime — AI screening/voice platform (credited by the customer story; no individual company representative named)