How AI Interview Copilots Work: Transcription, AI, and Stealth Architecture

The three-layer architecture of an AI interview copilot

Every AI interview copilot — whether it admits it or not — depends on three technical layers working together: audio capture, transcription, and answer generation. The quality and implementation of each layer determines the user experience and the stealth characteristics of the product.

• Layer 1: Audio capture — how the tool hears the interviewer
• Layer 2: Speech-to-text transcription — how it converts audio to searchable text
• Layer 3: AI answer generation — how it produces relevant answers or hints

Layer 1: Audio capture methods and their trade-offs

There are three main ways an AI interview tool can capture the interviewer's voice:

• Virtual microphone driver: installs a fake audio device that intercepts the audio stream. Visible in OS audio settings and video platform audio menus — high detection risk.
• Browser extension audio tap: hooks into the browser's Web Audio API to capture meeting audio. Visible as an extension to IT monitoring tools and may conflict with enterprise security policies.
• System audio session API (WASAPI on Windows): reads audio from the OS sound mixer without creating a new device. Not visible as a participant or device in the video platform. This is how MigMig works.

Layer 2: Transcription — latency and accuracy

Once audio is captured, it must be converted to text quickly enough to be useful before the candidate needs to respond. Speech-to-text models range from local on-device models (fast but lower accuracy) to cloud-based ASR APIs (higher accuracy but dependent on network latency).

MigMig targets approximately 150 ms from speech completion to text display. This uses an optimized transcription pipeline that balances accuracy and speed. A question like 'Tell me about a time you had to influence someone without authority' should be transcribed and displayed within roughly a third of a second of the interviewer finishing the sentence.

Layer 3: AI answer generation

The transcribed question text is sent to a language model context that is specialized by interview mode. For behavioral questions, the context includes STAR-method scaffolding. For system design questions, it includes common architectural patterns and dimension prompts. For coding questions, it includes algorithm category classification and hint structure.

The AI does not generate a monologue for the candidate to read. It generates structured talking points, scaffold elements, or algorithm hints — short, actionable, and adaptable to the candidate's own voice. This is a deliberate design choice: verbatim reading is slower and sounds unnatural; structured hints enable authentic delivery.

Stealth: the invisible constraint on every design choice

Every architectural decision in a real interview copilot is constrained by stealth requirements. A virtual microphone captures audio more easily but creates a detectable artifact. A browser extension is simpler to distribute but visible to enterprise IT. A cloud transcription API is faster but creates outbound network traffic that advanced monitoring could flag.

MigMig's architecture is built stealth-first: WASAPI audio capture, local pre-processing, and an overlay rendered through a non-shared window surface. The 20+ stealth checks per session are the validation layer that confirms each component is behaving as intended. See stealth mode explained for the full check list.

What separates good and bad AI interview copilot implementations

A poor implementation: joins the meeting as a bot, uses a virtual audio device, requires a browser extension, and renders an overlay that appears in screen shares. Each of these is detectable by default.

A well-designed implementation: reads system audio natively, does not join the meeting, uses a screen-share-excluded overlay, and validates its stealth posture on each session launch. MigMig is designed around the latter approach. For a market comparison, see best AI interview tools comparison.

Frequently asked questions

How does an AI interview copilot hear the interviewer?

The best implementations capture system audio through the OS audio API (WASAPI on Windows) without creating virtual audio devices. This reads the interviewer's voice from the meeting application's audio output without appearing in the platform's audio settings.

How fast does an AI interview copilot transcribe speech?

MigMig targets approximately 150 ms from speech-to-text display. Total latency from speech to AI answer hint depends on the complexity of the question and the AI processing pipeline, but the transcription component is designed to keep up with natural speech pace.

Can an AI interview copilot generate complete answers or only hints?

MigMig generates structured talking points and scaffold elements rather than complete verbatim answers. This is intentional: structured hints enable natural delivery and reduce the risk of sounding robotic, while still providing the structural guidance candidates need under pressure.

Ready to try it? Download MigMig for free or see pricing.