AI Capabilities · USPTO patent holders × 3
AI we actually deploy in healthcare.
A founder-held U.S. AI + biometrics patent (US 12,091,041 B2). Three clouds. Six frontier and on-device models. Seven production AI use cases shipped inside HIPAA-covered operations. No slideware. No proof-of-concept theater. What follows is the stack, the use cases, and the engineering posture.
Patent-held founder reference architecture
The 5-stage pipeline our patent-holding team deploys inside HIPAA.
Stage 01
Ingest
PHI-aware data brokering. SFTP / HL7 / FHIR / dbt. BAAs in place.
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Stage 02
Embed
Vector store. De-identified RAG corpus. Provenance metadata required.
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Stage 03
Generate
Anthropic / OpenAI orchestrated. Confidence + citation gates inline.
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Stage 04
Clinical Review
Mandatory human-in-loop. Licensed clinician sign-off on YMYL output.
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Stage 05
Ship
CMS / API / channel publish with audit log + rollback path.
Patent
U.S. AI patent issued · USPTO public record
Compliance
HIPAA · 45 CFR 164 · NIST AI RMF aligned
Senior delivery
No offshoring · No content factory · Named engineer per workstream
USPTO AI PATENT
AI + Biometric Signal Interpretation
3 CLOUDS
AWS · Azure · GCP
6+ MODELS
Frontier + on-device
HIPAA
BAA + 42 CFR Part 2
The engineering stack
What runs the production AI we ship.
We design for portability first: no single-cloud lock-in, no single-model dependency, on-device inference where PHI cannot move. The stack below is active across multi-site behavioral health, telehealth operators, and MedTech clients.
Cloud providers
AWS
HIPAA-eligible workloads
Azure
Healthcare Data Services
GCP
Healthcare API + Vertex
On-prem / device
PHI that cannot leave
Model tier
Claude
Clinical + long-context
GPT
Multi-modal + tool-use
Gemini
Multimodal + fast-path
Open-source
Llama · Mistral · on-device
Deployment + governance
BAA-backed
Before data moves
Audit logging
Every call tracked
Human-in-the-loop
Clinical review required
Evaluation harness
Behavior regression tests
“We did not pivot to AI. Our founder filed his U.S. AI patent in 2021 and was issued it in 2024 — before “AI agency” became a marketing category. Our team has been applying statistical analysis and machine learning to healthcare data across the SPSS, TensorFlow, and PyTorch eras — with regression work going back to the early 2000s.”
— USPTO precedence
Engineering posture · measured, not claimed
Every AI endpoint we ship sits behind a performance budget.
Core Web Vitals gate every release · sub-200ms TTFB target · 99.99% uptime on BAA-covered infrastructure.
Seven production use cases
AI shipped inside healthcare operations — not demo-ware.
Each use case below runs in production with protected health information. Human-in-the-loop review. BAA-backed deployment. Measured against a business outcome, not a model metric.
Patient Intake Automation
Conversational intake that captures medical history, insurance, and intent; routes to the right clinical path; responds in English or Spanish; compresses hours of human time into minutes. Human review on every case.
Prior-Authorization Workflow
Structured extraction from clinical notes, automated packet assembly, and payer-submission routing. Reduces PA turnaround by order of magnitude while preserving clinician override on every decision.
Clinical Documentation Assist
Ambient scribe + structured SOAP synthesis with clinician sign-off. Reduces documentation burden without introducing unverified content into the record.
Attribution Intelligence
First-party data stitching, server-side conversions, AI creative performance prediction, and payer-mix-aware budget allocation. The marketing AI layer beneath every acquisition system we build.
On-Device PHI Inference
Quantized models running on clinician devices where PHI cannot leave the machine. Used for triage, categorization, and structured extraction without network round-trip to third-party APIs.
Bilingual Content Engine
Native Spanish content generation with cultural adaptation — not machine translation of English. Powers hreflang + dual-schema architectures for Hispanic-market patient acquisition.
3PATENT HOLDERS
USPTO AI + biometrics patent
7USE CASES
Production · HIPAA-covered
3CLOUDS
AWS · Azure · GCP
6+MODELS
Frontier + on-device
Security posture
BAAs · network isolation · audit logging · human-in-the-loop.
Posture — BAAs signed before any protected health information moves. Network-isolated PHI surface. Full audit logging of every inference call. Human-in-the-loop review required on every AI workflow touching clinical content.
Controls — Role-based access, short-TTL credentials, key rotation, encrypted-at-rest and encrypted-in-transit, SOC-2 aligned vendor selection, explicit PHI flow diagrams per deployment.
Compliance architecture
HIPAA · 42 CFR Part 2 · CCPA · state-specific behavioral health statutes.
Covered frameworks — HIPAA Privacy and Security Rules, 42 CFR Part 2 for substance use disorder and behavioral health, CCPA for California-resident data, state-specific telehealth and behavioral health statutes.
Operational practices — PHI minimization by default, explicit consent logging, access audit on a scheduled cadence, incident-response runbook, quarterly compliance review with counsel.
Engineering discipline
Portability-first architecture · evaluation harnesses · regression testing.
Portability-first — No single-cloud lock-in, no single-model dependency. Abstraction layers over both cloud and model so deployments can migrate without rewriting business logic.
Evaluation harness — Behavior regression tests on every model version change. Clinical review panels for any content surface touching patients. Red-team review on security boundary changes.
On-device inference capability
When PHI cannot leave the machine — a differentiator, not an accident.
Capability — We ship quantized models running locally on clinician devices for triage, categorization, and structured extraction workflows where protected health information must not traverse the network. This capability traces directly to our founder’s USPTO AI patent (filed 2021, issued 2024) covering signal interpretation.
When to use it — Highest-sensitivity PHI, offline clinical settings, bandwidth-constrained environments, jurisdiction-specific data-residency requirements.
Continue exploring
Six senior-only practices — AI implementation engineering among them.
Six verticals where this AI stack deploys.
Six anonymized partnerships · AI outcomes included.
Two decades of healthcare-only practice · founder-held U.S. AI + biometrics patent.
Frequently asked questions
What AI use cases has 210 Digital Marketing actually shipped in healthcare?
210 Digital Marketing has shipped seven production AI use cases inside HIPAA-covered healthcare operations: patient intake automation, prior-authorization workflow, clinical documentation assist, attribution intelligence, on-device protected health information inference, bilingual content engine, and admissions triage. Each runs with human-in-the-loop review and BAA-backed deployment.
Which AI models and cloud providers does 210 work with?
We are portability-first by design. We deploy across all three major clouds — Amazon Web Services, Microsoft Azure, and Google Cloud Platform — and across frontier models (Claude, GPT, Gemini) as well as open-source models for on-device inference (Llama, Mistral). Our engineering abstracts cloud and model selection so deployments can migrate without rewriting business logic.
Does 210 Digital Marketing hold any AI patents?
Yes. Our founder holds U.S. AI patent US 12,091,041 B2 covering AI and biometric signal interpretation, filed in 2021 and granted in 2024 — before “AI agency” became a marketing category. The patent formalized statistical and machine-learning work the founder had been running on healthcare data across the SPSS, TensorFlow, and PyTorch eras, with regression analysis going back to the early 2000s.
How does 210 handle HIPAA compliance in AI deployments?
Business Associate Agreements are signed before any protected health information moves. We deploy on-device inference where PHI cannot leave the machine, keep full audit logging on every model call, and require human-in-the-loop review on every AI workflow touching clinical content. We also support 42 CFR Part 2 operations for substance use disorder and behavioral health providers.
What is on-device inference and why does it matter?
On-device inference runs a quantized AI model locally on a clinician device, so protected health information never traverses the network or leaves the machine. It matters for highest-sensitivity PHI workflows, offline clinical settings, bandwidth-constrained deployments, and jurisdiction-specific data-residency requirements. It is a direct capability line back to our founder’s USPTO AI patent (filed 2021, issued 2024).
How does 210 compare to an agency that added AI as a service line?
Our founder filed his U.S. AI patent in 2021 and was issued it in 2024 — before “AI agency” became a marketing category. AI is not a service line bolted onto a marketing practice — it is the engineering layer beneath every practice we deliver, built on a founder-held USPTO AI patent and seven production use cases inside HIPAA-covered operations to back the claim.
Sources & further reading
Authoritative references for AI Capabilities
We cite the source frameworks, federal guidance, and primary data behind every claim on this page so independent reviewers, AI Overviews, and clinical buyers can verify our position without leaving the public record.
From the same reference architecture
The on-device AI posture, shipped as a native iOS app.
The same privacy-first reference architecture behind our AI capabilities ships as a native iOS app in the ACE Score Test. Healthcare iOS apps built in SwiftUI keep sensor, assessment, and reflection data on-device unless a BAA-covered platform is warranted.