Built Secure for Behavioral Health

1. Leadership Rooted in Cybersecurity and Online Safety

Founder & CEO Ross Young brings over 15 years of leadership in cybersecurity and ethical AI. Prior to founding Clinically AI, Ross served as the EVP of North America of a pioneering AI safety platform for K–12 education, helping lead global product strategy and North American operations. That platform used natural language processing and AI to detect early signs of suicide, school violence, sexual abuse, and substance use disorder in real time — complying with the strictest cybersecurity standards across multiple countries. Today, that system protects over 23 million students across the globe.

Clinically AI’s experience blends deep technical proficiency with an uncompromising commitment to safety and privacy. This foundation of AI built to protect the most vulnerable now drives our mission in behavioral health: a platform engineered from the ground up to be secure, responsible, and aligned with clinical realities.

2. Secure Architecture & Zero Data Retention (ZDR)

Security is not a feature of our platform — it’s a foundation. Clinically AI is designed to meet and exceed regulatory and ethical standards across every layer:

SOC 2 Certified: Our systems, operations, and internal controls are independently audited for security, availability, and confidentiality, including penetration testing.

HIPAA-Compliant Infrastructure: All protected health information (PHI) is handled in accordance with HIPAA guidelines.

Zero Data Retention (ZDR): No session data — including audio — is stored after processing. Audio is instantly transcribed and destroyed. The resulting text can be optionally retained based on the organization’s preferences in a secure, private cloud environment.

Model Isolation: Our models are not trained on customer data and cannot access PHI after interaction. This means our models cannot be poisoned, compromised, or exposed through traditional data-leakage vectors. Clinically AI is built for zero-trust environments, giving administrators total control and full transparency.

3. De-Identification via Safe Harbor Method

When training or fine-tuning models, we never use customer data. Instead, we rely on a separate, rigorously managed dataset composed of de-identified content, processed using the Safe Harbor Method — the gold standard in healthcare data protection under HIPAA. This method systematically removes 18 key identifiers, ensuring the remaining data cannot reasonably be linked to a specific person. We only use de-identified data after receiving explicit approval from the contributing organization, and only for the agreed-upon scope of improvement.

4. Clinician-in-the-Loop Verification

Artificial intelligence must be accountable to clinical standards. Every model used in Clinically AI undergoes Clinician-in-the-Loop (CITL) verification — a structured process involving:

• Expert review of model outputs by licensed behavioral health professionals
• Scenario testing across various clinical contexts
• Pre-release testing for hallucination, inconsistency, and bias detection
• Comparison to clinical documentation benchmarks set by organizations, payers, and accrediting bodies

We do not release models until they have been fully verified against the clinical tasks they are meant to support.

5. AI That Supports, Never Supersedes Clinicians

Clinicians are the ultimate decision-makers. Our system enforces structured documentation through organization-defined templates, but clinicians retain the ability to:

• Adjust tone, phrasing, and language style to reflect their voice
• Tell the AI how to use contextual information
• Provide clinical judgment and observations to the AI
• Ask the AI for alternative phrasing or content adjustments that reflect the nuance of care

Importantly, clinicians cannot modify organizational prompts or compliance logic — protecting the integrity of organizational standards and ensuring consistent documentation quality across providers.

6. Standards Set by Organizations, Not Vendors

Every behavioral health agency operates within a unique constellation of payer contracts, state regulations, licensing requirements, accrediting bodies, and clinical philosophies. Instead of imposing rigid rules, we enable organizations to define their gold standards:

• Required fields, structures, length, and template sections
• Terminology, regulatory requirements, and tone
• Fidelity expectations and documentation scoring logic
• Rules by service line, modality, or funding stream

Our AI becomes an extension of your policies, not a substitute for them.

7. Multi-Model Infrastructure Built for Task-Specific Accuracy

One model does not fit all in healthcare. Clinically AI employs a multi-model architecture that leverages the strengths of different AI models for specific clinical tasks — from medical-terminology models to proprietary behavioral-health models, fine-tuned reasoning models running in HIPAA-compliant, Zero-Data-Retention environments, and GraphRAG for accurate, consistent, context-aware outputs. Each model operates within strict usage boundaries defined by our ZDR and compliance infrastructure.

8. U.S.-Based Development and Data Sovereignty

All development and operations — engineering, product, clinical validation, and support — are based in the United States, and all customer data remains in U.S.-based cloud environments. Our architecture adheres to data-sovereignty laws, ensuring no data is transferred across borders or subjected to foreign access. This guarantees full compliance with HIPAA, FERPA, and U.S. data-protection statutes, elimination of international access risk, and transparent accountability within the U.S. legal framework.