Structured Risk Data Collection with Voice AI — Without the Manual Workload
Vokalith conducts structured insurance evaluation interviews with applicants, collecting the risk data your underwriters need — accurately, consistently, and at scale.
4×
Faster evaluation intake
99%
Complete risk data per evaluation
40%
Lower intake cost per application
The Challenge
Why manual intake fails here
Insurance evaluation intake is high-volume and repetitive, yet errors in risk data collection have downstream consequences for underwriting accuracy. Manual intake creates bottlenecks and inconsistent records.
Incomplete risk data
Manual applicant interviews miss fields, require callbacks, and produce inconsistent records across coordinators.
Slow turnaround
Evaluation intake delays hold up underwriting workflows and extend time-to-quote for applicants.
Coordinator capacity limits
High evaluation volumes require staffing levels that drive administrative costs without adding analytical value.
Manual underwriting system entry
Risk data collected by phone is manually entered into underwriting systems, introducing transcription errors.
How Vokalith Helps
What the automated workflow looks like
Structured applicant interview
AI conducts a complete evaluation interview — applicant history, risk factors, prior claims, coverage needs — following your intake logic.
Adaptive questioning
Interview branches dynamically based on responses, collecting additional detail where risk indicators are identified.
Structured evaluation record
A complete, structured evaluation record is generated and delivered to underwriting for immediate review.
Underwriting system integration
Intake data is written directly to your underwriting platform — no manual re-entry, no transcription errors.
Capabilities
What's included
- Structured applicant risk interviews
- Adaptive risk-factor questioning
- Prior claims history capture
- Underwriting system integration
- Complete structured evaluation records
- Audit trail on every application
- Outbound interview scheduling
Related
Related Use Cases
Insights
