root@mindgraph:~# cat case-studies/elite-standard-screening-automated.md
Elite-Standard Screening, Automated
DOCUMENT: Elite-Standard Screening, Automated
INDUSTRY: HR Tech
PROJECT: AI-Powered Screening
CLASSIFICATION: CASE STUDY // PUBLIC RELEASE
SUMMARY:
A two-layer AI interviewer that generates candidate-specific questions, delivers a live voice interview, and outputs a SELECTED/REJECTED recommendation with AI-written rationale.
DETAIL:
Resume parsing → hardest-first scenario questions unique to the candidate → live voice interview with adaptive follow-ups → per-skill scoring with confidence ratings and a weighted final recommendation.
IMPACT:
SCREENING STANDARD: Consistent bar, every candidate
11 / HR TECH / AI-POWERED SCREENING
Client: Enterprise recruiting clients
Tags: HR Tech · Recruiting · Voice AI · Adaptive Interviewing · Automated Scoring
// Problem
Screening candidates at volume meant choosing between generic scripted questions that never actually tested what was on a resume, or spending real recruiter time per candidate to interview sharply and judge consistently. Vague or rehearsed answers routinely got through because nobody had the time — or a consistent enough standard — to push back.
// The Contribution
Built a two-layer AI interviewer. The candidate's resume is parsed into a structured profile — skills, domain, experience level — and matched against the job description to generate a set of hardest-first, multi-skill scenario questions that are specific to that candidate, not generic. The interview itself is delivered as a live voice conversation, with the resume fed to the voice agent as context so it can reference specifics naturally. Every answer is evaluated against strict elite-hire standards: vague or evasive answers get exactly one retry before being scored as a fail. As the interview progresses, follow-up questions escalate in difficulty based on the candidate's own previous answer.
// Technical Details
Resume-to-Questions
LLM-based resume parsing produces a structured candidate profile (skills, domain, experience level), which is matched against the job description to generate 8 hardest-first, multi-skill scenario questions unique to that candidate.
Live Voice Delivery
The interview runs as a real-time voice conversation via an AI voice agent, with the parsed resume passed in as live context so questions and follow-ups can reference it naturally.
Adaptive Difficulty
Follow-up questions are generated dynamically based on the candidate's previous answer, escalating difficulty and integrating multiple skills rather than repeating a fixed script.
Scoring & Reporting
Every answer is scored 1-5 with vagueness detection (one retry, then scored as failing) and per-skill confidence ratings. Skills are weighted by importance × confidence into ranked strengths and weaknesses, and the final score is normalized against a configurable pass threshold for a SELECTED/REJECTED recommendation with AI-written rationale.
- Fallback logic ensures a usable report even if AI commentary generation fails