Technology that serves everyone must understand everyone. Yet AI systems continue to reflect the biases and blind spots of their creators, especially when it comes to marginalized communities.
As a Public Benefit Corporation, social good is built into our DNA. We’re committed to creating AI that truly sees, hears, and responds to all people with empathy and accuracy, starting with Black culture. Our mission goes beyond profit—we’re dedicated to building technology that actively advances equity and inclusion.
By combining advanced machine learning with deep cultural expertise, we’re creating a new approach to AI that prioritizes cultural context alongside technical performance, ensuring our work generates measurable social impact for underrepresented communities.
We’re starting our journey by focusing on the experiences of Black culture—a community that faces some of the most severe gaps in AI recognition and representation.
Through extensive research, community partnerships, and proprietary datasets, we’re building systems that understand the linguistic patterns, cultural references, and lived experiences that shape Black culture.
Our phased approach ensures that we can deeply understand specific community needs before expanding to serve additional marginalized groups in Phase II.
We prioritize the protection of user data with industry-leading privacy measures.
End-to-end encryption for all sensitive data
Minimized data collection and retention
Transparent data usage policies
Regular security audits and updates
Our systems are designed to identify and address biases that disproportionately impact marginalized communities.
Diverse training datasets with cultural context
Continuous monitoring for fairness across demographics
Regular bias audits with community partners
Transparent reporting on model limitations
We believe users should have meaningful control over their data and AI interactions.
Opt-in data sharing with clear explanations
User-friendly privacy controls
Right to be forgotten and data portability
Transparency in AI decision-making processes
