Map the workflow and risk
We document users, decisions, data sources, PHI boundaries, human-review points, and the consequences of an incorrect output.
Output: workflow map, data boundaries, and an initial risk register.
Healthcare AI development and integration
Secure AI features built for real healthcare workflows
From useful prototype to governed production system
AI that fits your product, data, users, and compliance obligations
Alluxi designs and builds production AI for healthcare platforms. We map the workflow first, then select the right model and architecture. Our teams implement private knowledge assistants, document processing, workflow automation, and human-reviewed decision support with access controls, evaluation, monitoring, and HIPAA-aware data handling designed in from day one.
Healthcare AI use cases
Where we help healthcare teams apply AI safely
Private RAG assistants for clinical and operational knowledge
Patient intake, triage, and administrative workflow automation
Document extraction, classification, and summarization
Human-reviewed decision support with clear escalation paths
LLM evaluation, monitoring, guardrails, and auditability
Secure integration with EHR, FHIR, and existing product workflows
TOOLS
Our tech stack
Deep experience across diverse technology stacks. We pick the right tools for your project and deliver solutions that scale.
Huggingface
Langchain
OpenAI
PyTorch
How we deliver healthcare AI
A testable path from discovery to production
We do not start by choosing a model. We start with the workflow, the risk, and a measurable definition of quality.
Prototype with evaluation from day one
We build a bounded prototype and representative test cases to measure task quality, unsupported claims, safety, latency, and cost.
Output: measurable prototype, evaluation dataset, and acceptance criteria.
Engineer controls and oversight
We implement role-based access, cited sources, human approval, escalation, audit logs, retention rules, and fallback behavior.
Output: production architecture, controls, and a validation plan.
Launch, monitor, and improve
We release in stages, monitor quality and cost, version prompts and models, and evaluate every change before expanding use.
Output: an operable system with metrics, traceability, and an improvement process.
