Problem solved

Capifiq addresses the surge in AI-driven cheating in remote interviews, where candidates use invisible overlays, proxy test-takers, and deepfake identities that evade traditional screen-sharing and detection tools. It provides deterministic evidence of tool presence, replacing unreliable behavioral probability scores and enabling hiring teams to make informed, defensible decisions.

Use cases

• Detecting AI cheating tools during live remote video interviews
• Verifying candidate authenticity in high-stakes technical hiring
• Providing timestamped, hashed evidence of tool usage for hiring disputes
• Training interviewers to recognize and mitigate off-device cheating methods
• Integrating with existing recruitment workflows to automate integrity checks
• Screening candidates for prohibited AI assistance in real-time coding assessments

Audience

• Enterprise
• Startups
• Business Users

Pros

• Tool-agnostic detection works against any cheating tool, including unknown or renamed ones
• Provides deterministic evidence rather than ambiguous probability scores
• Live detection during interviews with tool naming and activity timestamps
• Combines software detection with training to combat off-device cheating vectors
• Patent-pending approach differentiates from conventional behavioral inference

Cons

• Paid subscription required beyond a limited free tier
• Requires installation or integration on the interview platform or candidate machine
• Cannot detect entirely out-of-band cheating (e.g., a person in the room not using software)
• Effectiveness against future evasion techniques may require ongoing updates
• Primarily designed for remote interviews, not in-person settings