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A voice and text screening assistant that interviews candidates, scores against a role rubric, and generates bias-aware reports for human review.
Strong decomposition with practical tradeoffs.
Good state model; needs deeper testing examples.
Clear, structured, concise answers.
The assistant asks role-specific questions, captures answers, evaluates competencies, and drafts a report that recruiters can review quickly.
A standardized first pass that keeps humans in control and gives teams better evidence faster.
ASR and real-time hints help candidates stay on track, with text fallback supported.
LLM evaluates evidence against your rubric with calibrated scores.
PII redaction, sensitive-attribute filters, and human review before decisions.
Role pipelines, pass-through rates, and time-to-decision reporting.
Templates for engineering, product, support, and sales with tailored question banks.
Clear consent, timeline, expectations, and language hints for FR, EN, and AR.
A stack designed for reliable interviews, defensible reports, and privacy-aware hiring workflows.
LLM orchestration
Rubric scorer
Follow-up planner
Streaming ASR
Entity extraction
PII filters
Next.js chat UI
Realtime timers
Report generator
PostgreSQL
Object storage
Encryption at rest
Recruiters get consistent evidence without removing human judgment from the hiring process.
From days to hours with a consistent first pass.
Evidence-linked ratings improve human debriefs.
Standardized interviews reduce variance and bias.
Standardize screening, reduce time to decision, and improve candidate experience.