Alexey Litvin | CEO
Trusted by:
Public AI models can risk exposing sensitive patient data, violating HIPAA regulations and compromising trust
With public models, you lose oversight of how sensitive data is stored and processed
Public AI tools are vulnerable to cyberattacks, increasing the risk of data leaks and hefty penalties
Public AI models can produce unpredictable and inconsistent results affecting patient care quality and trust in your organization
Relying on public AI models for healthcare-related purposes can pose significant risks
Built for US Healthcare providers to withstand cyber threats and retain complete control over data processing
results:
Up to 63% of physicians report symptoms of burnout, reducing workforce retention
AI tools save 2 hours daily per clinician, enabling 20-25% more patient visits. Burnout rates drop by 30%, improving retention.
In 2024, new patients in the U.S. waited an average of 26 days to see a doctor, with 71% seeking more personalized care and 30% citing poor communication as reasons for dissatisfaction
Boost engagement by 40%, cut wait times by 30%, and improve treatment adherence by 20-25%, fostering trust through personalized and efficient care.
Hospital inefficiencies waste 25-30% of resources and drive costs, with facility and staffing expenses making up nearly 60% of healthcare expenditures
AI reduces costs by 20-25% and resource waste by 25-30%. Streamlined operations enhance patient satisfaction scores by 15-20%, with smoother transitions and reduced delays.
AI-driven billing reduces administrative costs by 40%, decreases denial rates by 30-50%, and boosts patient satisfaction by 20-30% through transparent billing systems.
The AI-Powered Virtual Health Assistant provides:
We deployed the AI system directly within the organisation’s infrastructure, ensuring that all sensitive patient and operational data remains secure, fully controlled, and compliant with internal policies and healthcare regulations
By fine-tuning the AI model with the organisation’s existing data, we made it highly precise and responsive to the specific needs of the organisation, ensuring more accurate and context-specific outputs
We designed an intuitive interface that allows teams to ask questions in plain language and receive clear, actionable insights. This empowers decision-makers to retrieve key insights without relying on technical experts
The implementation delivered significant advancements in operational efficiency and routine automation, achieving the following outcomes:
Faster access to critical data enabled quicker decision-making and care delivery
By automating repetitive tasks, healthcare professionals could redirect their focus to high-value, patient-centered activities
Improved workflows and seamless access to information led to higher-quality care and increased patient satisfaction
10+
Years experience in Healthcare domain
Anton
Avramenko
Lead of AI Research
Vitalii
Samarskyi
Solution Architect
Lena
Kirienko
Head of Product
Alexey
Litvin
CEO, Founder
AWS certified
AWS certified
Azure OpenAI
Global AI community
Meta AI
Department focused on AI research
GitHub
Copilot
LangChain
AWS certified
AWS certified
Azure OpenAI
Global AI community
Meta AI
Department focused on AI research
GitHub
Copilot
LangChain