Healthcare AI Advisory

AI Readiness Assessment

Comprehensive evaluation of your organization's data infrastructure, team capabilities, and regulatory posture for AI/ML adoption.

Executive roadmap with prioritized initiatives and ROI projections.

ML Strategy & Roadmap

Define your 12-24 month AI/ML vision aligned with business objectives, clinical workflows, and compliance requirements.

Includes build-vs-buy analysis and vendor landscape review.

Board/Executive AI Briefing

Demystify AI for leadership. Covers opportunity identification, risk landscape, competitive positioning, and investment considerations—tailored to healthcare and pharma.

Production ML Architecture Review

Deep-dive audit of your existing ML systems for scalability, reliability, and compliance gaps.

Prioritized remediation plan with effort estimates.

HIPAA/PHI Data Pipeline Audit

End-to-end review of data flows, access controls, encryption, and audit logging.

Identifies compliance vulnerabilities before they become findings.

MLOps Maturity Assessment

Evaluate your model lifecycle management, CI/CD practices, monitoring, and drift detection against industry benchmarks.

Maturity scorecard with gap analysis.

Cloud Cost & Efficiency Review

Identify waste and optimization opportunities in your ML infrastructure across AWS, Azure, or GCP.

Typical savings: 20-40% reduction in compute spend.

FDA 510(k) AI/ML Preparation

Guidance on documentation, validation protocols, and submission strategy for Software as a Medical Device (SaMD).

Includes predetermined change control plan framework.

RAG System Compliance Framework

Design and implement guardrails for retrieval-augmented generation in regulated contexts—MLR review integration, hallucination mitigation, audit trails, and human-in-the-loop workflows.

Clinical AI Governance Design

Establish policies, review boards, and monitoring protocols for responsible AI deployment in clinical settings.

Aligns with emerging FDA and ONC guidance.

Production Deployment Sprint

Hands-on leadership to take a validated model from development to production-ready deployment with monitoring, alerting, and rollback capabilities.

Team Scaling & Hiring Strategy

Define roles, interview frameworks, and organizational structure for building or expanding your ML engineering team.

Includes compensation benchmarking.

Vendor Selection & Due Diligence

Structured evaluation of AI/ML vendors, platforms, or acquisition targets.

Technical deep-dive plus commercial and integration risk assessment.