AI Interview Assistant: How Real-Time Interview Help Works

A real-time AI interview assistant is a software system that captures live audio from a video call, converts speech to text using advanced speech-to-text models, processes the transcription through a large language model to generate contextual answers, and displays those answers on a transparent overlay — all within a fraction of a second. Aissence's implementation achieves end-to-end latency of just 116 milliseconds from the moment the interviewer stops speaking to when a tailored answer appears on screen. This performance is made possible by a carefully optimized pipeline that combines system-level audio capture, streaming speech recognition, edge-deployed inference, and a native desktop rendering layer that operates independently of the browser environment.

The Audio Pipeline

The foundation of any AI interview assistant is audio capture. Aissence uses system-level audio interception rather than microphone capture, meaning it records the interviewer's voice directly from your computer's audio output stream. This approach eliminates background noise, echo, and the quality degradation that comes with capturing audio through a microphone pointed at speakers. On Windows, this uses WASAPI loopback capture; on macOS, it leverages Core Audio with a virtual audio device.

Speech-to-Text Processing

Once audio is captured, it flows into a streaming speech-to-text engine. Unlike batch transcription that waits for a complete utterance, streaming STT processes audio chunks in real time, producing partial transcriptions that update as more audio arrives. Aissence uses an optimized Deepgram integration that delivers word-level timestamps and speaker diarization, enabling the system to distinguish between your voice and the interviewer's voice.

LLM Inference and Response Generation

The transcribed question is immediately sent to a large language model along with context — your resume, the job description, your preparation notes, and the conversation history. The LLM generates a structured answer framework using streaming output, so the first tokens appear on screen while the model is still generating. Aissence's 116ms response time achievement came from optimizing every stage: pre-warming model connections, using speculative decoding, and deploying inference at the edge closest to the user.

The Native Overlay Display

Latency Breakdown

• Audio capture to STT — ~30ms for chunk processing
• STT finalization — ~40ms for end-of-utterance detection
• LLM first token — ~35ms with pre-warmed connections
• Overlay render — ~11ms for native rendering

For job seekers preparing for interviews, understanding this pipeline helps you configure Aissence optimally. Start with the preparation checklist, practice with mock interviews, and ensure your remote interview setup is optimized for the best audio capture quality. Visit safety documentation to understand the private architecture in detail.

Follow the interview or assessment rules

Use AI tools for preparation unless the employer explicitly permits assistance during the session. Do not use software to conceal prohibited help or bypass integrity controls. Review the Aissence responsible-use policy when the rules are unclear.