Transform Patient Care with 
AI-Powered Healthcare Solutions

Our AI platforms handle diverse healthcare data types including medical imaging (DICOM format for X-rays, MRIs, CT scans), structured EHR data (lab results, vital signs, medications), unstructured clinical notes and physician documentation, genomic and biomarker data, wearable device outputs and IoT sensor data, claims and billing information, and patient-reported outcomes. We build custom data pipelines that normalize, clean, and prepare your data for AI model training while maintaining HIPAA compliance throughout.

Investment depends on AI complexity, data requirements, and integration scope. Small AI solutions (single-use case like chatbot or documentation assistant) range from $50,000-$100,000. Medium-complexity projects (predictive analytics with EHR integration) run $100,000-$200,000. Large-scale platforms (multi-model AI with imaging, NLP, and decision support) typically cost $200,000-$500,000+. We provide transparent estimates after assessing your specific requirements and existing infrastructure.

Security and compliance are built into our AI architecture from day one. We implement AES-256 encryption for all PHI used in training and inference, de-identification protocols for model development, role-based access controls, comprehensive audit logging, and secure cloud infrastructure (HIPAA-eligible AWS/Azure). Our SOC 2 Type II and ISO 27001 certifications ensure enterprise-grade security. Every AI model undergoes penetration testing and HIPAA audits before deployment.

Absolutely. We've integrated with 50+ EHR platforms including Epic, Cerner, Allscripts, athenahealth, and eClinicalWorks. We use industry-standard protocols like HL7 v2.x for ADT feeds and results, FHIR R4 for modern interoperability, and custom APIs when needed. Our integration approach maintains data integrity, enables real-time data pipelines, and doesn't disrupt your clinical workflows. We handle the complexity so your teams don't have to.

Our clients typically see measurable ROI within 6-12 months. Common outcomes include 30-40% reduction in administrative costs through automation, 25-35% decrease in hospital readmissions via predictive monitoring, 40-60% faster diagnostic interpretation with AI imaging, 50-70% reduction in documentation time using clinical NLP, and improved claims processing with 15-25% fewer denials. We establish clear KPIs upfront and track performance throughout deployment to ensure you hit your targets.

We take algorithmic fairness seriously. Our approach includes diverse training datasets representing varied demographics, regular bias detection audits across patient populations, fairness metrics monitoring (demographic parity, equal opportunity), explainable AI frameworks showing decision logic, and clinical validation with real-world patient data. We test models across different age groups, ethnicities, and socioeconomic backgrounds to ensure equitable performance. Any bias detected triggers immediate model retraining before production deployment.

Yes, AI models require continuous optimization to maintain accuracy. Our MLOps platform provides automated model monitoring, performance analytics tracking degradation, drift detection identifying when retraining is needed, scheduled retraining with new clinical data, A/B testing for model improvements, and compliance updates as regulations evolve. We offer 24/7 support with dedicated account management. Think of it as preventive maintenance for your AI infrastructure to keep performance optimal.

Definitely. We have experience developing FDA 510(k) and De Novo pathway-ready AI as Software as Medical Device (SaMD). Our services include regulatory strategy consulting, clinical validation study design, quality management systems per ISO 13485, technical documentation for FDA submissions, risk management frameworks, and post-market surveillance protocols. We work with regulatory consultants and can guide you through the entire FDA approval process to ensure your AI solution meets all requirements.

Your data remains your property, always. We implement strict data governance: all training data is de-identified and anonymized, stored in HIPAA-compliant encrypted environments, accessed only by authorized personnel with signed NDAs, never shared with third parties or used for other projects, and deleted per your retention policies. You maintain complete ownership of both raw data and trained AI models. We can also deploy federated learning approaches where models train locally without data leaving your infrastructure.

Medical AI requires continuous learning to stay clinically relevant. We establish automated pipelines for regular model updates with latest clinical evidence, integration with medical literature databases and research publications, collaboration with your clinical teams for guideline changes, scheduled retraining cycles (typically quarterly or as needed), version control tracking all model iterations, and validation testing before deploying updates. Your AI stays current with evolving medical standards without manual intervention from your team.