Data Architecture & Strategy
- Enterprise-wide data architecture roadmaps
- Cloud, on-prem, and hybrid architecture patterns
- Blueprinting future-state AI adoption readiness
Our Services
Architecture-first consulting that bridges strategy and execution — helping healthcare organizations adopt AI as a tool that empowers people, not replaces them.
Architecture-first. We understand your business and current state before making any technology recommendations.
What We Do
Six core practice areas, delivered by experienced Enterprise Architects, Solution Architects, and Developers who specialize in healthcare technology. Scroll to move through each practice — or select one to jump straight to it.
AI Readiness Assessments
What's included
- Stakeholder interviews across business, clinical, and IT teams
- Review of current architecture, data assets, integrations, and reporting tools
- Identification of data challenges, gaps, and opportunities for AI enablement
- Benchmarking against healthcare standards and AI readiness frameworks
- Identification of risks, redundancies, and barriers to AI adoption
- Deliverable: Prioritized AI readiness report with actionable recommendations
AI Governance, Risk & Compliance
What's included
- Privacy frameworks aligned with PHIPA, PIPEDA, HIPAA, and GDPR
- SOC 2 and ISO 27001 alignment strategies
- AI ethics and transparency frameworks
- Threat modeling and security by design for AI systems
- Data stewardship models and governance operating structures
- Risk mitigation strategies to safeguard data used in AI initiatives
Enterprise Architecture & AI Roadmaps
What's included
- Enterprise integration across clinical, lab, finance, and HR systems
- Cloud-native solution design (Azure/AWS) and hybrid integration patterns
- Future state roadmaps and governance frameworks
- Data architecture and interoperability (FHIR, HL7, CDA, REST)
- Legacy modernization and system rationalization
- Sequenced roadmap with dependencies, costs, and timelines
Data Architecture & ML/AI Pipelines
What's included
- Enterprise-wide data architecture roadmaps aligned with clinical and business goals
- Centralized data lake/warehouse solutions for machine learning pipelines
- Standards-based integration (HL7, FHIR, API, ETL) connecting EMRs, LIS, HIS
- Interface engine development to unify fragmented and legacy data sources
- Data pipelines using Azure Data Factory, SSIS, and Databricks
- Master Data Management ensuring clean, reliable data for trustworthy AI
Custom AI Solution Design & Build
What's included
- Full-stack development (.NET Core, Python, Node.js, React/Angular)
- AI agent design with human-in-the-loop review workflows
- OCR/HTR, NLP entity extraction, and document processing automation
- FHIR-compliant outputs and healthcare interoperability standards
- Mobile apps (iOS, Android, cross-platform) for point-of-care use
- CI/CD pipelines using Azure DevOps and GitHub Actions
Healthcare IT Resource Augmentation
What's included
- Experienced Enterprise Architects embedded with your team
- Solution Architects and developers with healthcare domain expertise
- Integration specialists (HL7 engines, Mirth, Rhapsody)
- Agile delivery frameworks (Scrum, SAFe) with architecture reviews
- Automated testing (Selenium, NUnit, PyTest) and performance testing
- Flexible engagement models: advisory, staff augmentation, or delivery
Our Data Services
A comprehensive set of data capabilities that lay the foundation for AI adoption in healthcare.
Data Integration & Interoperability
- Standards-based integration (HL7, FHIR, API, ETL)
- Interface engine development for legacy systems
- Seamless exchange across regional and federal health initiatives
Data Governance & Quality
- Governance frameworks aligned with PHIPA, PIPEDA, HIPAA
- Data quality assessments covering accuracy and timeliness
- Master Data Management (MDM) for trustworthy AI
Analytics & AI Readiness
- Centralized data lake/warehouse for ML pipelines
- BI dashboards and predictive analytics for clinical KPIs
- AI readiness: data maturity, infrastructure, and compliance
Compliance & Security
- SOC 2 and ISO-aligned practices for health data
- Security risk assessments and role-based access
- Risk mitigation for AI and advanced analytics initiatives
Cloud & Infrastructure
- Azure landing zones and enterprise cloud setup
- Kubernetes (AKS) deployments with Helm
- Infrastructure as Code (Terraform, Bicep, ARM)
Our Method: Building the Foundation for AI
A structured, collaborative approach designed to deliver immediate value and long-term AI readiness.
01
Phase 1: Discovery & Current State Assessment
- Stakeholder interviews across business, clinical, and IT
- Review current architecture, data assets, and integrations
- Identify data challenges and AI opportunities
02
Phase 2: Data Maturity & Gap Analysis
- Assess governance, data quality, interoperability, and security
- Benchmark against healthcare standards and AI frameworks
- Identify risks and barriers to adoption
03
Phase 3: Future State Vision & AI-Ready Architecture
- Co-create target data and analytics architecture
- Scenarios for cloud adoption and AI/ML deployment
- Prioritized capability model for incremental adoption and ROI
04
Phase 4: Roadmap & Recommendations
- Sequenced roadmap with dependencies, costs, and timelines
- Quick-win opportunities for measurable value
- Long-term strategy aligned to AI adoption and digital health
Ready to Start Your AI Journey?
Let's assess your readiness and build a roadmap together.