AI Interview SaaS - 10K+ Sessions/Month, 99% Satisfaction

Traditional technical screening interviews are time-consuming for senior engineers, prone to human bias, and inconsistent across candidates. Candidates face long delays scheduling initial rounds, leading to high drop-off rates during the hiring process.

We built an automated, real-time AI technical interviewer capable of conducting adaptive voice/text interviews, assessing coding ability live, evaluating soft skills, and delivering structured, unbiased scorecard reports within minutes.

Real-Time Voice & Chat Interviewer Low-latency conversational AI engine simulating a human interviewer with adaptive follow-ups based on candidate answers. The system doesn't read from a script; it listens, processes, and probes deeper on weak areas while escalating difficulty on strong responses.

Live Coding Sandbox & Proctoring Embedded code editor supporting multiple programming languages, real-time code execution, complexity analysis, and anti-cheating signals. Candidates write and run code during the interview while the AI evaluates approach, correctness, and efficiency.

Custom Persona & Rubric Builder Employers configure custom scoring rubrics tied to role seniority. A System Design interview for a Staff Engineer uses different evaluation criteria than an Algorithm-focused screen for a junior candidate. The AI adapts its persona and question depth accordingly.

Automated Candidate Assessment Reports Structured scorecard generation with code evaluation, audio sentiment analysis, confidence scores, and verbatim transcriptions. Hiring managers get a complete picture within minutes of the interview ending.

Frontend: React / Next.js with WebRTC / WebSockets for low-latency bi-directional voice and streaming text output.

Backend: Node.js / Python (FastAPI) microservices architecture running on AWS (ECS / Lambda).

AI Orchestration: LLM chaining (GPT-4o / Claude 3.5 Sonnet) combined with custom system prompts for strict interview guardrails and evaluation accuracy.

Voice Subsystem: Whisper AI for real-time Speech-to-Text and ElevenLabs / Deepgram for ultra-low latency Text-to-Speech.

Code Execution Engine: Isolated, containerized Docker sandboxes (AWS Fargate) to evaluate candidate code safely against test cases without remote code execution risks.

Phase 1: Discovery & Core Engine PoC (Weeks 1-4) Benchmarked STT/TTS latencies and LLM prompt structures. Built a minimal CLI-based streaming voice prototype to validate the core conversational loop before investing in UI.

Phase 2: MVP Development (Weeks 5-12) Built the React frontend, WebRTC audio streaming pipeline, Docker sandbox runner, and employer dashboard. This was the heaviest sprint: wiring real-time voice, code execution, and AI scoring into a single coherent session flow.

Phase 3: Beta Testing & Tuning (Weeks 13-16) Onboarded early-adopter tech teams. Refined LLM prompt guardrails to reduce hallucinations and calibrated scoring rubrics against human recruiter baseline evaluations. This phase was critical for trust: if the AI scores didn't correlate with human judgment, the product wouldn't work.

Phase 4: Scaling & Security Compliance (Weeks 17-20) Optimized Docker auto-scaling, implemented SOC2/GDPR data security for candidate recording retention, and launched public release.

10,000+ sessions per month with 99% user satisfaction

Time-to-hire reduction: Average candidate initial screening time dropped from 7 days to under 24 hours

120+ engineering hours saved per month per company on screening interviews

Voice-to-voice latency under 800ms, delivering natural conversational flow

94% interview completion rate, significantly higher than async text-only assessment tools