Case studies
Deployments. Measured outcomes.
Historical, company-reported results, with workflow and customer context.
Recruitment Response Automation
How conversational AI transformed applicant response and recruitment throughput.
92 → <2 min
Initial applicant response time
Approximate time from application to first contact, before and after the workflow.
3.4x
More interviews
Increase in interviews progressed through the documented recruitment workflow.
34%
Inactive candidates reactivated
Share of previously inactive candidates who re-engaged through outbound reactivation.
27%
Reduction in no-shows
Reduction in candidates failing to attend scheduled interviews.
Historical, company-reported results from specific deployments. Not independently audited and not a guarantee of current or future performance.
Operational challenge
High applicant volume created delays between application submission and initial recruiter engagement. The team needed a more consistent process for contacting, screening and progressing applicants.
Previous workflow
Recruiters contacted applicants manually as capacity allowed. First contact could take well over an hour, and screening questions and notes varied between recruiters.
Solution implemented
iDial implemented a voice AI workflow that engaged applicants, conducted structured screening conversations and supported the progression of qualified candidates toward interviews.
How the AI agent operated
- 01Triggered by new applications entering the recruitment system
- 02Introduced the role and confirmed the applicant was available to talk
- 03Asked structured screening questions with follow-ups
- 04Captured availability, experience and preferences
- 05Directed qualified applicants toward interview scheduling
Integration and workflow
Application events triggered calls; call outcomes, transcripts and screening summaries were returned to the recruitment team's workflow.
Applicant enters ATS
New application event
Workflow triggers call
Outbound within minutes
AI screening call
Structured questions
Structured result
Scorecard back to ATS
Business impact
Applicants were engaged while their interest was highest, and recruiters spent more time with candidates who had already passed a consistent initial screen.
Candidate Reactivation
Turning an inactive candidate database back into recruitment pipeline.
34%
Inactive candidates reactivated
Share of previously inactive candidates who re-engaged during the campaign.
Historical, company-reported results from specific deployments. Not independently audited and not a guarantee of current or future performance.
Operational challenge
Many candidates had registered or been screened previously but never progressed. Their availability and interest were unknown and records were out of date.
Previous workflow
Re-engaging the database depended on recruiters finding time between live roles, so most of it went untouched.
Solution implemented
An automated outbound campaign called selected candidate segments, confirmed current availability and interest, and updated records.
How the AI agent operated
- 01Called segments of previously inactive candidates
- 02Asked whether they were currently available for work
- 03Confirmed interest and employment preferences
- 04Captured updated information
- 05Routed interested candidates to recruiters
Integration and workflow
Campaign outcomes and refreshed candidate details were recorded for recruiter follow-up.
Business impact
Existing records became an active source of candidates, with updated availability data and interested candidates identified for recruiters.
High-Volume Multilingual Calling
Scaling multilingual voice interactions without proportionally scaling manual call handling.
5
English/Spanish voice agents
Configured agents deployed in parallel.
30,000+
Calls handled
Total calls handled across the deployment.
~17% → 0.2–0.3%
Missed or dropped interactions
Reported share of missed or dropped interactions before and during the deployment.
Historical, company-reported results from specific deployments. Not independently audited and not a guarantee of current or future performance.
Operational challenge
Call volume exceeded what the team could handle consistently, and a significant share of interactions were missed or dropped. Contacts needed to be served in both English and Spanish.
Previous workflow
Calls were handled manually, and capacity limits led to missed or dropped interactions.
Solution implemented
Five configured English/Spanish voice agents ran structured call workflows, with performance monitored throughout the deployment.
How the AI agent operated
- 01Five voice agents configured for English and Spanish
- 02Structured call workflows for consistent handling
- 03Language-appropriate conversations
- 04Ongoing performance monitoring
Integration and workflow
Call outcomes and activity were monitored and reported across the deployment period.
Business impact
The operation handled high call volumes in two languages while sharply reducing missed or dropped interactions. The documented three-month project value was approximately $9,000.
Explore the work.
The platform and its historical applications.