Naitur.Ai provides a compliance and management dashboard specifically designed for psilocybin service centers and facilitators, automating client intake, scheduling, and regulatory reporting while ensuring HIPAA-level data security. This platform addresses the challenges of maintaining compliance with evolving legislation and streamlining operational workflows, allowing providers to focus on client care.
Funding
$5.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Psilocybin service centers face challenges in managing client intake, scheduling, and regulatory reporting while ensuring data security and compliance with evolving legislation. These operational complexities can divert resources from client care and increase the risk of non-compliance.
Solution
Naitur.Ai provides a compliance and management dashboard designed for psilocybin service centers and facilitators, automating key operational workflows. The platform streamlines client intake, facilitator and room scheduling, and data reporting, ensuring consistent adherence to state and federal regulations. It offers tools to manage client notifications, educational documents, forms, and assessments, all while maintaining HIPAA-level data security to protect participant privacy. By automating these processes, Naitur.Ai enables providers to focus on client care and reduce business stress.
Target Audience
The primary target audience includes licensed psilocybin facilitators, psilocybin service centers, and researchers in the field of psychedelic-assisted therapy.
Features
- Client intake and screening automation
- Facilitator and room scheduling tools
- Automated generation and distribution of educational documents and forms
- Secure data storage with HIPAA-level security
- Customizable client notifications and reminders
- Data reporting tools for regulatory compliance
- Integration with research protocols and data models