Capabilities

Compliance, cloud platforms, and product delivery — from one team.

We connect product strategy, secure architecture, cloud infrastructure, and end-user delivery into a single effort, so the seams between them are ours to manage instead of yours.

Healthcare trust

HIPAA-aware delivery

Privacy, auditability, and operational safeguards are requirements from day one — not a hardening pass before go-live.

Access model Role Data scope PHI Coordinator assigned cases masked Clinician assigned cases full Analyst aggregate only none Integration de-identified export none least privilege · default deny Audit trail 14:02:11 clinician · opened case 8841 14:02:44 export · 41 records de-identified 14:03:02 denied · scope outside assignment
Access is scoped per role; every read, export, and denial is recorded.

Control model

  • Role-based access with least-privilege defaults
  • Encryption in transit and at rest across every system boundary
  • AI-assisted PHI detection and redaction before downstream sharing
  • Audit logging and traceability on all critical workflow actions

Implementation patterns

  • Environment separation for development, staging, and production
  • Data minimization for sensitive workflow processing
  • Policy checks for allowed disclosures and minimum-necessary access
  • Structured incident response and operational monitoring

Open source

io-dicom — healthcare interoperability

Our DICOM tooling, built in the open. It handles tag inspection and conformance, adds AI-assisted data quality checks, and exposes a natural language query layer so clinical and operational teams can interrogate imaging data without writing code.

Tag inspector Tag Keyword Value (0010,0020) PatientID (0008,0060) Modality CT (0020,000D) StudyUID mismatch (0028,0010) Rows 512 (0028,0100) BitsAllocated 16 (0008,0016) SOPClassUID Conformance 1 finding Referential fail Required tags pass De-identification Sensitive fields 7 all masked minimum necessary
Tag inspection, conformance checks, and sensitive-field masking.

Interoperability foundation

  • Reusable DICOM handling for custom applications and integrations
  • Designed to slot into broader healthcare automation stacks
  • Built to shorten the path from prototype to regulated production

Data quality and natural language queries

  • Detects tag mismatches, missing fields, and referential inconsistencies
  • Plain-English queries across study metadata — no custom SQL or scripting
  • Sensitive-field flagging to support de-identification and minimum necessary
  • Anomaly scoring so human review goes to the right studies first

Cloud and architecture

Kubernetes, microservices, and multi-cloud operations

Infrastructure build-out and ongoing operations across Azure, GCP, and AWS — designed so a bad deploy is boring rather than an incident.

Ingress edge · tls termination · waf healthy Cluster a · primary api 3/3 worker 6/6 ingest 2/3 Cluster b · standby api 3/3 worker 6/6 ingest 3/3 Data plane postgres ha object store crr secrets kms Rollout · canary error budget nominal · auto-rollback armed 25%
Multi-cluster topology with per-service health and guarded rollout.

Kubernetes delivery

  • Cluster design across the full environment lifecycle
  • Orchestration patterns for high-availability workloads
  • Observability, health checks, and rollout safety controls

Microservice-oriented systems

  • Service decomposition aligned to business capabilities
  • API-first contracts and event-driven integration
  • Fault isolation, versioning strategy, and incremental migration
Azure
  • Landing zone and resource organization strategy
  • Identity and access with least-privilege defaults
  • Observability and alerting for production reliability
Google Cloud
  • Project and environment segmentation by lifecycle stage
  • Secure service-to-service communication patterns
  • Cost and performance visibility across workloads
AWS
  • Network architecture with segmented security boundaries
  • Operational automation for deploys and rollbacks
  • High-availability and disaster recovery planning

Applied AI

Operational AI that keeps people in control

AI that augments a team without quietly taking over the decisions the team is accountable for.

Workflow discovery for AI fit

  • Task and decision-point mapping to find AI-ready stages
  • Risk tiering to define where automation is allowed or constrained
  • Data-readiness review for retrieval, classification, and summarization

Human-in-the-loop orchestration

  • Confidence-based routing from suggestion to reviewer queue
  • Approval gates and audit history for regulated decisions
  • Defined fallback when model confidence drops below threshold

Reliability and governance

  • Prompt and output observability tied to operational KPIs
  • Checks for drift, hallucination risk, and data leakage
  • Compliance evidence trails for audits and incident reviews
  • Release controls for staged prompt and model updates

Mobile

Apps built for field work, not demos

Native and cross-platform delivery shaped around how people actually use a phone mid-shift — often offline, often one-handed.

Native and cross-platform delivery

  • iOS and Android delivery for operational use cases
  • Cross-platform strategies for faster release cycles
  • Offline-aware workflows and sync-ready data handling

Enterprise-grade mobile architecture

  • Secure authentication and session lifecycle design
  • API-driven architecture aligned with the service layer
  • Release management for phased rollouts

Next

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We can shape the roadmap, the architecture, the infrastructure, and the delivery plan around the team you already have.

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