Coding & Skill Assessments
DeepScreen's assessment pipeline is gated — candidates must clear each stage before advancing. This ensures expensive recruiter time is reserved for candidates who have already demonstrated technical competence and communication skills through automated, proctored evaluation stages.
Assessment Pipeline
Coding Assessment
Judge0 CE + TauriCandidates solve timed coding challenges in a sandboxed Judge0 CE environment. Tauri desktop client provides OS-level process monitoring — detects AI copilots (ChatGPT desktop, GitHub Copilot), remote desktop tools (TeamViewer, AnyDesk), and browser automation (chromedriver, geckodriver). Code is compiled and tested against hidden test cases with time complexity analysis.
Gate: Score Threshold
ConfigurableCoding score must meet the minimum threshold for the specific role. Thresholds are configurable per position — a senior backend role requires 80%+ while a junior frontend role may accept 60%+. Candidates who pass advance to Stage 2. Candidates who fail receive a detailed performance report and are removed from the pipeline.
AI Avatar Interview
sherpa-onnx + Supertonic 33D avatar conducts a structured interview with adaptive questioning powered by Groq LLM (350-600ms response). Speech handled entirely offline: Web Speech API for STT, Supertonic 3 ONNX INT8 for TTS (99M parameters, 31 languages, CPU-only). LLM adapts question difficulty based on response quality — strong answers lead to harder follow-ups, weak answers trigger clarification questions.
Gate: Interview Score
Threshold CheckAI-assessed interview score must meet threshold. The LLM evaluates technical depth, communication clarity, and problem-solving approach. Scores include confidence intervals rather than single-point estimates — a candidate with 0.75 ± 0.05 is more reliably scored than one with 0.75 ± 0.15.
Live Recruiter
LiveKit SFUMulti-participant WebRTC session via LiveKit SFU. Audio latency: 80-150ms. Video latency: 120-250ms. Multi-user sync: <300ms. CPU usage: <15% with 4 participants. Recruiter sees the full assessment history and can ask targeted follow-up questions based on specific coding or interview performance gaps.
Final Report
Evidence PackComprehensive report combining: resume analysis summary, coding assessment scores with test case breakdown, AI interview transcript with confidence scores, proctoring evidence pack (gaze logs, head pose, object detection, process monitoring), and live interview notes. Weighted composite score with configurable weights per role type.
Proctoring Signals
| Signal | Method | Threshold |
|---|---|---|
Gaze Tracking | MediaPipe Face Mesh (468 landmarks) | Off-screen >30% frame time |
Head Pose | solvePnP with facial landmarks | Yaw/pitch ±20° for 5+ frames |
Face Presence | MediaPipe detection | Absent for 3+ consecutive frames |
Multi-Person | YOLOv8s ONNX INT8 | 2+ faces detected for 2+ frames |
Prohibited Objects | YOLOv8s ONNX INT8 | Phone/tablet confidence >0.5 |
Process Monitoring | Tauri sysinfo crate | Blocklist match → immediate flag |
Offline TTS Specifications
| Parameter | Value |
|---|---|
Parameters | 99 million |
ONNX Model Size | 404 MB total |
Languages | 31 |
Runtime | CPU-only, no GPU required |
Integration | sherpa-onnx Rust bindings |
Init Latency | <50ms |
Privacy | Fully offline, no network dependency |