AI-driven hiring and the science of compatibility

Thousands of voice biomarkers analyzed via neural networks

MAPA extracts pitch, jitter, shimmer, speech frequency, and linguistic density (verb usage, self-reference frequency) through proprietary L1 models to create behavioral profiles tied to real-world hiring outcomes.

Multi-context sampling prevents single-moment bias

The platform collects voice samples across different days and formats including WhatsApp voice notes and technical interviews to stabilize data and capture authentic behavior rather than interview performance.

Voice chosen over video for authenticity

Research revealed video caused candidates to 'act as they want to be perceived,' whereas voice—our primary socialization tool—provides a more authentic window into collaboration styles since people learn to speak before reading or writing.

Maps company culture, not just candidates

Unlike traditional tools, MAPA analyzes hiring managers' and stakeholders' voice patterns to create accurate behavioral profiles of actual company culture rather than relying on stated values or mission statements.

Compatibility over similarity matching

The algorithm matches based on how behavioral profiles complement each other rather than seeking identical traits, preventing clashes between similar high-intensity personalities while optimizing team dynamics.

Shortlists top 3 from extensive screening

While presenting only three final candidates to clients, the system screens large candidate pools to find optimal matches, with 60% of MAPA-suggested hires being women, LGBTQ+, or immigrants.

Accounts for accent and cultural communication differences

The platform recognizes that biomarkers like pitch and loudness are interpreted differently across cultures, training models to avoid penalizing non-native English speakers or diverse communication styles.

Latino-led perspective on linguistic bias

The team's experience with cultural differences ensures the AI understands that traits like loudness may signal confidence in some cultures but insecurity in others, preventing misinterpretation of behavioral signals.