Candidate accents, call quality, and specialty terminology vary
Healthcare / Delivered project
Voice AI for Healthcare Hiring
A supervised voice workflow for candidate outreach, structured screening, scheduling, and recruiter handoff.
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StatusDelivered project
IndustryHealthcare
SystemData → Intelligence → Action
01 / Problem statementDelivered project · Healthcare
The operating problem
Clinical hiring teams spend significant time coordinating repetitive calls while candidates wait for basic answers and next steps.
Conditions the system must survive03 operating constraints
Hiring workflows require fairness, consent, and recruiter oversight
Voice automation must hand off gracefully when intent or risk is unclear
02 / How we approached it
From operating uncertainty to testable evidence.
The work was decomposed into four engineering decisions. Each one produced an artifact the customer could inspect, test, and carry into deployment.
01Frame
Define approved conversation paths and escalation boundaries
Evidence produced
Approved conversation, consent, and escalation map
02Evaluate
Integrate telephony, scheduling, and applicant systems
Evidence produced
Speech benchmark across call conditions and role terminology
03Engineer
Structure responses without making hiring decisions
Evidence produced
Recruiter agreement study for structured qualification fields
04Operationalize
Route complex or sensitive conversations to recruiters
Evidence produced
ATS and scheduling integration with human handoff states
03 / Deployment record
Deployed around the workflow—not beside it.
The system boundary includes where inference runs, how evidence reaches existing tools, and how people handle uncertainty after launch.
Implementation statusDelivered project
01
TopologyCloud telephony and voice orchestration with isolated transcription, policy, and scheduling services.
02
IntegrationApproved job criteria, ATS records, calendars, and recruiter queues connect through role-scoped APIs.
03
OperationUnclear or sensitive conversations route to recruiters; consent, recording, field changes, and handoffs remain auditable.
04 / Evaluation metrics
What must be measured before the system earns trust.
Evaluation covers model behavior, workflow burden, and production performance. The metric defines the gate; the customer baseline and acceptance threshold define the target.
01Evaluation gate
Call completion rate
How it is measured
Consented calls reaching a valid completion or human handoff.
What it decides
Measures whether the workflow is usable without trapping candidates.
02Evaluation gate
Structured-field accuracy
How it is measured
Agreement between extracted answers and recruiter review.
What it decides
Sets which ATS fields can be suggested or require confirmation.
03Evaluation gate
Qualification agreement
How it is measured
Agreement on rule-based routing decisions with recruiter adjudication.
What it decides
Validates policy implementation without automating hiring judgment.
04Evaluation gate
Candidate effort
How it is measured
Time to completion, abandonment, repeat questions, and handoff delay.
What it decides
Protects the customer and candidate experience.
05 / Customer perspective
Value has to appear in the customer’s operating day.
What matters in practice
The assistant should explain its role, collect only approved information, and make human help immediately available.
06 / Technology context
Tools follow the system—not the other way around.
Final architecture depends on data quality, operating conditions, integrations, risk, and evaluation criteria established during discovery.
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