Zero Assist

What is Zero Assist?

Zero Assist is a real-time interview integrity monitor for remote technical interviews. It installs a lightweight forensic agent on the candidate’s machine during a session and watches for known AI cheating tools, suspicious overlay behavior, audio routing anomalies, and virtual machine environments.

The product is designed for engineering teams, technical recruiters, and hiring managers who want evidence of what happened during an interview without interrupting the conversation. When it detects a flagged process or other suspicious activity, it alerts the interviewer and records details for a post-session forensic report.

Key Features

• Real-time detection of 20+ AI cheating tools, including ChatGPT, Parakeet AI, Final Round AI, Cluely, Interview Coder, LockedIn AI, and GitHub Copilot.
• OS-level process monitoring, which checks the candidate’s running processes rather than relying only on screen sharing.
• Live interviewer dashboard with WebSocket alerts delivered in under 500 milliseconds, so interviewers can respond during the session.
• Detection of screen overlays, audio routing anomalies, and virtual machine environments, expanding coverage beyond visible applications.
• Session-based agent that runs during the interview and exits when the session ends, with no persistent installation described in the source.
• Post-session forensic reports with timestamps, flagged process names, severity levels, and evidence logs for review or dispute resolution.
• Configurable data retention and automated deletion, with GDPR compliance stated on the source page.

How to Use Zero Assist

A typical workflow starts with creating a free account and generating a session code. The interviewer then sends the agent download link to the candidate before the interview.

At the start of the session, the agent runs on the candidate’s machine and monitors for flagged tools or suspicious activity. Interviewers follow the live dashboard during the call, then review the forensic report after the session if an alert was triggered.

Use Cases

• Engineering interviews where teams want to check whether candidates are using AI assistance during live coding or problem-solving.
• Technical recruiting workflows that need a record of flagged activity for review, audit, or candidate follow-up.
• Hiring managers running remote interviews who want live alerts instead of relying only on screen sharing.
• Data science interviews where candidates may be tempted to use external AI tools during hands-on tasks.
• Dispute review after an interview when the team needs timestamps and evidence logs to assess what happened.

FAQ

Does Zero Assist take screenshots or keep a permanent agent installed? The source says it captures no screenshots and exits when the session ends, with no persistent installation described.

What kinds of activity can it detect? It checks for known AI cheating tools and also looks for screen overlay patterns, audio routing anomalies, and virtual machine environments.

How are alerts delivered? Alerts appear in a live interviewer dashboard through a WebSocket connection, and high-severity alerts also trigger an audio chime.

Is there a free way to try it? Yes. The source says new accounts receive free credits and no credit card is required to start.

What does the product report after an interview? It provides a forensic report with the flagged process name, severity, timestamp, and evidence logs.

Alternatives

• Manual screen sharing review: simpler, but it only shows what the candidate chooses to display and cannot reliably detect background tools or overlays.
• General interview proctoring software: often focused on broad monitoring, while Zero Assist is positioned specifically around technical interview integrity and AI-cheating detection.
• Post-interview code review alone: useful for assessing output quality, but it does not provide live detection during the session.
• Other remote hiring monitoring tools: may cover identity or general session control, but may not include the same process-level AI tool detection described here.