AI Capabilities
Two issued patents. Three clouds. Six frontier models. Zero slideware.
Healthcare executives can find a hundred agencies offering “AI-powered” services. They will find very few who have actually filed a patent, shipped production AI inside HIPAA environments, and run cloud, open-source, and on-device inference workloads simultaneously. This page is for the CIO, CMIO, CTO, or operating partner doing the technical diligence.
Thesis
Implementation is the differentiator — because implementation is where almost everyone stops.
There is a wide gap between an agency that talks about AI and a team that ships AI inside production healthcare workflows. The market is crowded with the first group. The second is a much smaller room. We have been in it for twenty years — filing patents, deploying across enterprise cloud estates, and proving outcomes that survive a healthcare audit.
Our capability is not generic large-language-model prompting. It is the end-to-end work of deploying AI inside regulated healthcare environments — architecture selection, model selection, compliance posture, integration with existing EHR and CRM systems, measurement, and the operational handoff that lets your team run the system after we are gone.
Implementation is a discipline. Slideware is a pitch. We do the first one.
Patent Portfolio
Two issued U.S. patents. Not applications — granted.
Patent examiners do not care about buzzwords. Novelty, utility, prior art. The work that survives that filter is engineering lineage — and it informs how we think about every healthcare AI deployment we architect today.
Issued Patent · 01
AI + Biometrics
A patented method applying artificial intelligence to biometric identification and authentication workflows.
Healthcare relevance:
- Patient identity verification for telehealth and digital front door
- Consent capture with fraud-resistant authentication
- HIPAA-grade access control for PHI workflows
- Step-up authentication inside clinical applications
Issued Patent · 02
Mixed Reality System
A patented mixed-reality system bridging physical and digital environments.
Healthcare relevance:
- Clinical training and simulation at scale
- Immersive patient education for complex procedures
- Provider workflow augmentation (hands-free clinical interfaces)
- Brand-grade patient experiences that differentiate on the front end
The Stack
Stack-agnostic by mandate. Proven across every major vendor.
Healthcare buyers inherit complex technology estates. We deploy inside whichever cloud and whichever model architecture fits the workload — measured by performance, cost, and compliance envelope, not by vendor relationship.
Cloud
Microsoft Azure
Enterprise healthcare estates, Microsoft 365 integration, Azure OpenAI.
Cloud
Amazon Web Services
HIPAA-eligible services, Bedrock for model orchestration, SageMaker for custom training.
Cloud
Google Cloud
Vertex AI, BigQuery, Google Ads Data Hub, Search Ads 360 integration.
Cloud
Private & Hybrid
On-prem Kubernetes and edge deployments where data residency is non-negotiable.
Frontier LLM
Anthropic Claude
Long-context reasoning, instruction following, code and document workflows. Primary pick for complex healthcare reasoning.
Frontier LLM
Google Gemini
Multimodal workloads, native Google Cloud integration, tight coupling with Vertex AI and search surfaces.
Frontier LLM
OpenAI GPT
Legacy integrations, tool-using agents, Azure-hosted deployment for enterprise Microsoft customers.
Open Source
Llama · Mistral · Qwen · DeepSeek
Fine-tuning, cost optimization, on-device deployment, and data sovereignty.
On-Device
Local inference hardware
For HIPAA-sensitive workloads where PHI cannot leave the device. Dedicated silicon with local models.
Vector
Pinecone · Weaviate · pgvector
Retrieval-augmented generation over your clinical, operational, and marketing knowledge base.
Data
Snowflake · BigQuery · Databricks
Analytics and feature stores that feed AI pipelines with governed data.
Integration
Epic · Athena · Salesforce Health Cloud · HubSpot
EHR, CRM, and marketing automation — AI outputs delivered where the workflow lives.
Use Cases Shipped
What we have deployed inside healthcare — in production.
A partial list of workload types we have engineered, measured, and handed off to operating teams. Specifics are covered by client NDA; architecture patterns are not.
UC · 01
Patient intake triage at scale
AI-assisted intake systems that route inbound patient inquiries to the right clinical track, qualify insurance and payer fit, and schedule admissions — compressing cycle times from hours to minutes while maintaining clinical-quality review at every step.
UC · 02
Prior authorization automation
Prior-auth drafting and packet assembly augmented with AI trained on payer-specific requirements and historical approval patterns — reducing clinician burden and accelerating care delivery.
UC · 03
Marketing content operations
AI-assisted content engineering across English and Spanish for SEO, AI Overview citation coverage, and paid-media creative testing — scaling editorial output 5–10× while keeping clinical accuracy and brand voice intact.
UC · 04
Clinical documentation augmentation
Ambient documentation and note-generation workflows deployed with provider oversight — reducing after-hours EHR work while producing audit-grade documentation output.
UC · 05
Attribution and measurement intelligence
First-party data pipelines, server-side tracking, and AI-enriched attribution that survives iOS privacy changes — feeding closed-loop reporting into Google Ads, Meta, and programmatic channels.
UC · 06
On-device PHI inference
Local-model deployments on dedicated hardware for workloads where protected health information cannot leave the device — enabling AI capability inside clinical settings previously considered out of reach.
UC · 07
Conversational clinical agents
Provider- and patient-facing conversational agents with retrieval grounded in your clinical knowledge base, deployed with escalation paths that always route sensitive interactions to human clinicians.
Security Posture
HIPAA-aware by default. HITRUST and SOC 2 pattern-literate.
We do not treat compliance as a checkbox at the end of implementation. We architect for it from hour one. Every AI deployment we design accounts for PHI boundaries, data residency, access control, audit logging, and retention — because every one of those failure modes can end a healthcare operator’s growth story overnight.
We work inside your existing compliance framework. We sign Business Associate Agreements. We respect the PHI firewall. For workloads that cannot send data off-device, we deploy local inference. For workloads that require full cloud, we engineer inside HIPAA-eligible services with encryption, network isolation, and auditable access patterns.
None of this is new for us. Healthcare has been the only industry we work in for two decades.
Principles
What we refuse to do — because most of the market still does.
We will not ship AI without measurement. If we cannot instrument the output and the business outcome, we do not deploy it. AI without measurement is theater.
We will not lock you into a vendor. Our recommendations are driven by workload fit and compliance posture, not fee-share agreements. If the right answer is an open-source model on your own hardware, that is the answer.
We will not hand over a system you cannot operate. Every engagement ends in documentation, runbooks, and a team trained to run what we built. Agency dependency is the opposite of the outcome we are selling.
We will not cross the clinical line. AI in healthcare augments clinicians. It does not replace them. We design with human-in-the-loop escalation at every touchpoint where patient safety is at risk.
Begin
If you are evaluating AI inside your healthcare organization — the fastest way to see whether we fit is a 60-minute intake.
Bring the workload you are considering. We will tell you what we would build, what it would cost in total ownership terms, and which parts we believe you should not outsource. No deck. No pitch.