AI-Ready Data Infrastructure
for Healthcare:
Fix Your Data.
Then Fix Everything Else.
Healthcare AI needs complete patient context from fragmented systems, unified in real time. We build the data infrastructure that makes AI applications accurate, safe, and scalable in clinical environments.
The Real Problem with Healthcare AI
The Real Problem with
Healthcare AI
It's not the algorithms. It's the data.
It's not the algorithms.
It's the data.
AI needs complete patient context to be clinically useful. Not just structured EHR fields, but clinical notes, imaging reports, lab results, medication histories, social determinants, and external care records. It needs this context unified, current, and accessible in real time.
Most health systems don't have this. Patient data sits scattered across incompatible systems. Data warehouses update nightly while clinical decisions happen in minutes. AI built on this fragmented foundation will be inaccurate regardless of model sophistication.
The bottom line: You can't deploy reliable healthcare AI without solving the data infrastructure problem first.
What AI-Ready Data Actually Looks Like
What AI-Ready Data
Actually Looks Like
What AI-Ready Data Actually
Looks Like
Unified Patient Context Layer
Unified Patient Context Layer
Instead of this:
Instead of this:
Patient data scattered across EHR, imaging PACS, lab system, billing platform, and paper records. Clinicians manually piece together the full picture.
Patient data scattered across EHR, imaging PACS, lab system, billing platform, and paper records. Clinicians manually piece together the full picture.
You get this:
You get this:
A 360-degree patient view combining all data sources into a single comprehensive source that preserves clinical context, maintains temporal relationships, and structures information for both AI consumption and human validation.
A 360-degree patient view combining all data sources into a single comprehensive source that preserves clinical context, maintains temporal relationships, and structures information for both AI consumption and human validation.
Intelligent Data Pipelines
Intelligent Data Pipelines
Instead of this:
Instead of this:
Manual data extracts, custom integrations that break with every EHR update, batch processes that run overnight, missing data that goes undetected.
Manual data extracts, custom integrations that break with every EHR update, batch processes that run overnight, missing data that goes undetected.
You get this:
You get this:
Automated ingestion from HL7 FHIR, CCDA, X12, and proprietary EHR APIs. Built-in PHI detection and de-identification. Real-time updates for time-sensitive data. Error handling that catches and routes exceptions without breaking workflows.
Automated ingestion from HL7 FHIR, CCDA, X12, and proprietary EHR APIs. Built-in PHI detection and de-identification. Real-time updates for time-sensitive data. Error handling that catches and routes exceptions without breaking workflows.
Vector-Optimized Storage
Vector-Optimized Storage
Instead of this:
Instead of this:
Keyword searches that miss clinically relevant information. Inability to find similar cases or related context. AI applications limited to structured data fields.
Keyword searches that miss clinically relevant information. Inability to find similar cases or related context. AI applications limited to structured data fields.
You get this:
You get this:
Hybrid architecture combining relational databases for structured data with vector databases for semantic search. AI can find clinically relevant information based on meaning, not just exact matches. Essential for RAG implementations and clinical reasoning support.
Hybrid architecture combining relational databases for structured data with vector databases for semantic search. AI can find clinically relevant information based on meaning, not just exact matches. Essential for RAG implementations and clinical reasoning support.
The difference?
AI-ready data is unified, contextual, and accessible in real time. Most healthcare data today isn't.
The Four Problems We Solve
The Four Problems We Solve
The Four Problems We Solve
01.
Fragmented Systems
The Issue:
Patient data lives in Epic, Cerner, imaging PACS, lab interfaces, billing systems, and legacy platforms. Each system uses different formats and standards.
Our Solution:
Integration layers that connect these systems without requiring replacement. FHIR-compliant APIs that provide standardized access regardless of underlying source systems.
What Changes:
AI applications access complete patient context through a single unified interface instead of custom point-to-point integrations.
01.
Choose a Plan
The Issue:
Patient data lives in Epic, Cerner, imaging PACS, lab interfaces, billing systems, and legacy platforms. Each system uses different formats and standards.
Our Solution:
Integration layers that connect these systems without requiring replacement. FHIR-compliant APIs that provide standardized access regardless of underlying source systems.
What Changes:
AI applications access complete patient context through a single unified interface instead of custom point-to-point integrations.
01.
Choose a Plan
The Issue:
Patient data lives in Epic, Cerner, imaging PACS, lab interfaces, billing systems, and legacy platforms. Each system uses different formats and standards.
Our Solution:
Integration layers that connect these systems without requiring replacement. FHIR-compliant APIs that provide standardized access regardless of underlying source systems.
What Changes:
AI applications access complete patient context through a single unified interface instead of custom point-to-point integrations.
02.
Incomplete Context
The Issue:
AI sees isolated data points. Diagnosis codes without clinical notes. Lab values without medication context. Current visit without relevant history.
Our Solution:
Intelligent synthesis that assembles fragments into coherent patient narratives. Temporal relationship preservation showing how conditions, treatments, and outcomes relate over time.
What Changes:
AI applications work with the same complete clinical picture that informs human decision-making.
02.
Incomplete Context
The Issue:
AI sees isolated data points. Diagnosis codes without clinical notes. Lab values without medication context. Current visit without relevant history.
Our Solution:
Intelligent synthesis that assembles fragments into coherent patient narratives. Temporal relationship preservation showing how conditions, treatments, and outcomes relate over time.
What Changes:
AI applications work with the same complete clinical picture that informs human decision-making.
02.
Incomplete Context
The Issue:
AI sees isolated data points. Diagnosis codes without clinical notes. Lab values without medication context. Current visit without relevant history.
Our Solution:
Intelligent synthesis that assembles fragments into coherent patient narratives. Temporal relationship preservation showing how conditions, treatments, and outcomes relate over time.
What Changes:
AI applications work with the same complete clinical picture that informs human decision-making.
03.
Compliance Complexity
The Issue:
HIPAA requirements, state privacy laws, and patient consent rules make data aggregation both technically and legally challenging.
Our Solution:
Compliance built into architecture from day one. HIPAA-compliant access controls, audit logging, automated consent management, and PHI detection pipelines.
What Changes:
You can deploy AI applications confidently knowing data governance meets regulatory requirements.
03.
Compliance Complexity
The Issue:
HIPAA requirements, state privacy laws, and patient consent rules make data aggregation both technically and legally challenging.
Our Solution:
Compliance built into architecture from day one. HIPAA-compliant access controls, audit logging, automated consent management, and PHI detection pipelines.
What Changes:
You can deploy AI applications confidently knowing data governance meets regulatory requirements.
03.
Compliance Complexity
The Issue:
HIPAA requirements, state privacy laws, and patient consent rules make data aggregation both technically and legally challenging.
Our Solution:
Compliance built into architecture from day one. HIPAA-compliant access controls, audit logging, automated consent management, and PHI detection pipelines.
What Changes:
You can deploy AI applications confidently knowing data governance meets regulatory requirements.
04.
Data Lag
The Issue:
Clinical decisions happen in minutes. Most data warehouses update nightly or weekly. Time-sensitive AI applications can't work with stale data.
Our Solution:
Real-time streaming for critical data elements combined with batch processing for comprehensive historical context. Hybrid approach balances freshness with completeness.
What Changes:
AI applications access current patient state while maintaining complete historical context for clinical reasoning.
04.
Data Lag
The Issue:
Clinical decisions happen in minutes. Most data warehouses update nightly or weekly. Time-sensitive AI applications can't work with stale data.
Our Solution:
Real-time streaming for critical data elements combined with batch processing for comprehensive historical context. Hybrid approach balances freshness with completeness.
What Changes:
AI applications access current patient state while maintaining complete historical context for clinical reasoning.
04.
Data Lag
The Issue:
Clinical decisions happen in minutes. Most data warehouses update nightly or weekly. Time-sensitive AI applications can't work with stale data.
Our Solution:
Real-time streaming for critical data elements combined with batch processing for comprehensive historical context. Hybrid approach balances freshness with completeness.
What Changes:
AI applications access current patient state while maintaining complete historical context for clinical reasoning.
How We Build This
01
Assessment and Architecture (Weeks 1-2)
We start by understanding your current state and designing infrastructure tailored to your specific needs.
What happens:
Inventory of data sources and integration points
AI readiness gap analysis
Reference architecture design
Technical requirements documentation
What you get:
Clear picture of current limitations, technical roadmap, and implementation plan with realistic timelines.
Assessment and Architecture (Weeks 1-2)
We start by understanding your current state and designing infrastructure tailored to your specific needs.
What happens:
Inventory of data sources and integration points
AI readiness gap analysis
Reference architecture design
Technical requirements documentation
What you get:
Clear picture of current limitations, technical roadmap, and implementation plan with realistic timelines.
Assessment and Architecture (Weeks 1-2)
We start by understanding your current state and designing infrastructure tailored to your specific needs.
What happens:
Inventory of data sources and integration points
AI readiness gap analysis
Reference architecture design
Technical requirements documentation
What you get:
Clear picture of current limitations, technical roadmap, and implementation plan with realistic timelines.
Implementation (Weeks 3-12)
This is where we build production infrastructure, not prototypes.
Core components:
FHIR-compliant data integration from source systems
Vector database setup for semantic search
RAG-ready knowledge bases with medical terminology mapping
Real-time patient context APIs for AI applications
Delivered incrementally:
Working systems every 2-3 weeks, not a big-bang deployment at the end.
Implementation (Weeks 3-12)
This is where we build production infrastructure, not prototypes.
Core components:
FHIR-compliant data integration from source systems
Vector database setup for semantic search
RAG-ready knowledge bases with medical terminology mapping
Real-time patient context APIs for AI applications
Delivered incrementally:
Working systems every 2-3 weeks, not a big-bang deployment at the end.
Implementation (Weeks 3-12)
This is where we build production infrastructure, not prototypes.
Core components:
FHIR-compliant data integration from source systems
Vector database setup for semantic search
RAG-ready knowledge bases with medical terminology mapping
Real-time patient context APIs for AI applications
Delivered incrementally:
Working systems every 2-3 weeks, not a big-bang deployment at the end.
02
03
Governance and Security (Ongoing)
Security and compliance aren't final gates. They're engineering requirements throughout.
Built-in from start:
HIPAA-compliant access controls and audit logging
Patient consent management workflows
Data quality monitoring and alerting
Automated PHI detection pipelines
Continuous operation:
These systems run perpetually, not just at launch.
Governance and Security (Ongoing)
Security and compliance aren't final gates. They're engineering requirements throughout.
Built-in from start:
HIPAA-compliant access controls and audit logging
Patient consent management workflows
Data quality monitoring and alerting
Automated PHI detection pipelines
Continuous operation:
These systems run perpetually, not just at launch.
Governance and Security (Ongoing)
Security and compliance aren't final gates. They're engineering requirements throughout.
Built-in from start:
HIPAA-compliant access controls and audit logging
Patient consent management workflows
Data quality monitoring and alerting
Automated PHI detection pipelines
Continuous operation:
These systems run perpetually, not just at launch.
Technology Partnerships
That Reduce Risk
Technology Partnerships That Reduce Risk
Technology Partnerships
That Reduce Risk
What You Actually Receive
What You Actually Receive
Why This Investment Compounds
Weak data infrastructure creates technical debt. Every AI application built on fragmented data becomes harder to maintain, slower to develop, and more expensive to operate.
Strong infrastructure does the opposite. Once you have unified patient context and intelligent pipelines in place each new AI application becomes:
Faster to develop (weeks instead of months)
Easier to validate (consistent data quality)
More likely to deliver clinical value (complete context)
Less expensive to maintain (shared infrastructure)
Weak data infrastructure creates technical debt. Every AI application built on fragmented data becomes harder to maintain, slower to develop, and more expensive to operate.
Strong infrastructure does the opposite. Once you have unified patient context and intelligent pipelines in place each new AI application becomes:
Faster to develop (weeks instead of months)
Easier to validate (consistent data quality)
More likely to deliver clinical value (complete context)
Less expensive to maintain (shared infrastructure)
You shift from fighting data problems to solving clinical problems.
Common Questions
Common
Questions
How much does comprehensive evaluation cost compared to the AI system itself?
Can't our AI vendor handle evaluation?
When should we start evaluation?
What if we've already deployed AI without systematic evaluation?
How do we know if our evaluation is sufficient?
How much does comprehensive evaluation cost compared to the AI system itself?
Can't our AI vendor handle evaluation?
When should we start evaluation?
What if we've already deployed AI without systematic evaluation?
How do we know if our evaluation is sufficient?
How much does comprehensive evaluation cost compared to the AI system itself?
Can't our AI vendor handle evaluation?
When should we start evaluation?
What if we've already deployed AI without systematic evaluation?
How do we know if our evaluation is sufficient?