Healthcare AI development for hospitals, clinics, and medtech.

Aiinfox engagements are HIPAA-aligned for healthcare clients. BAAs are signed before any engagement touches PHI. Self-hosted Llama 3 deployments inside customer VPC are standard for zero data egress. Audit logs cover every model call. SOC 2 Type II is in progress. We work with US and international healthcare clients across hospital chains, clinics, healthtech SaaS, and medtech devices.

For HIPAA-scoped engagements, patient data never leaves your network. We deploy Llama 3 70B or 8B on vLLM inside your AWS, Azure, or GCP VPC, or on-prem on your hardware. Self-hosted vector store (pgvector inside your existing Postgres, or Qdrant) keeps retrieval local. Reference deployment: a medical-inquiry RAG agent running fully inside hospital VPC with zero internet egress.

Five layers. Hybrid retrieval grounds every answer in your clinical corpus. Required citations link every claim to a source document. A refusal layer activates explicitly on safety-critical categories (drug dosage, contraindications, triage) — the system says "I cannot answer — escalating to a clinician" rather than guessing. Confidence scoring routes low-confidence answers to human review. An eval harness blocks any change that regresses safety-critical accuracy.

Yes. We fine-tune open-weight models (Llama 3, Mistral) on de-identified clinical data via LoRA, running entirely inside your environment. Reference: a fine-tuned Llama 3.1 for healthcare inquiries deployed self-hosted in a customer VPC for zero data egress. Reproducible pipelines with versioned datasets and weights, re-runnable as your clinical guidelines evolve.

No. Every Aiinfox healthcare deployment is built so the AI assists, scribes, summarises, retrieves, and flags — but every clinical decision remains with the clinician. The system defers (explicit refusal) on safety-critical queries and routes to a human. This is not just compliance theatre — it is the only design that clinicians actually adopt long-term.

Six to eight weeks for a clinical chatbot or RAG agent pilot. Eight to twelve weeks for AI HMS implementation in a single facility (including migration from HL7 / FHIR / legacy HMS and clinician training). Twelve weeks for a fine-tuned healthcare LLM with curated training set and self-hosted deployment. Multi-facility rollouts replicate per site after the first.

Most healthcare AI v1 engagements at Aiinfox land between $40,000 and $180,000 fixed-price depending on regulatory scope, integration complexity, and whether fine-tuning is required. AI HMS deployments are priced per-bed or per-clinician with mid-size facilities landing between $1,800 and $6,500/month all-in. On-prem deployments include a one-time setup and an annual support agreement.

HL7 v2 and FHIR R4 for clinical interoperability — we migrate from and integrate with major EMR / HMS systems via these protocols. DICOM for imaging. ICD-10, SNOMED CT, RxNorm, LOINC for coding. HIPAA Privacy + Security rules for US deployments, GDPR for EU, DPDP for Indian clients. SOC 2 Type II in progress.

Yes. Roughly half of our healthcare engagements run under a non-US regulatory perimeter. UK engagements are scoped against UK GDPR, the Data Protection Act 2018, and NHS DSPT expectations. Canadian engagements run under PIPEDA plus the relevant provincial health-privacy statute. Australian engagements run under the Privacy Act 1988, the APPs, and TGA guidance where the AI output touches a clinical decision directly. The engagement mechanics stay the same everywhere we work — signed data-processing agreement first, in-region or self-hosted inference, audit logging built for the specific regulator, clinician review checkpoint before production exposure.

The line is whether a clinician reviews the output before it changes patient care. A tool that surfaces a citation, drafts a note, or flags a document for review is clinical decision support. A tool that produces a diagnosis, a treatment recommendation, or a triage acuity that drives action without a clinician in the loop crosses into Software as a Medical Device territory — regulated by the FDA in the US, the MHRA in the UK, the TGA in Australia, and Health Canada in Canada. We design every deployment to stay on the decision-support side of that line by default, and we tell clients on the first call when a request is actually asking for a medical device, because that build belongs inside a regulatory affairs pathway we support as an engineering partner, not as the regulatory authority.

Epic, Cerner (Oracle Health), Athenahealth, NextGen, eClinicalWorks, and Meditech in the US; most NHS Trust systems and UK GP platforms that expose FHIR R4 or HL7 v2 interfaces; and the major Canadian and Australian hospital information systems built on the same HL7/FHIR foundation. We integrate through the standard the platform already exposes rather than screen-scraping or maintaining a shadow database — FHIR R4 resources for modern integrations, HL7 v2 ADT/ORU feeds for legacy interfaces, SMART on FHIR where the tool needs to launch inside the clinician's existing session.

Data stays in the region the regulation requires, and inference follows it. US engagements typically pin to AWS us-east-1 or self-host inside the customer's VPC. UK and EU engagements pin to a UK or EU cloud region with UK GDPR-aligned processing terms. Canadian engagements default to a Canadian AWS region where PIPEDA or provincial rules expect data to stay in-country. Australian engagements pin to AWS ap-southeast-2 (Sydney). Where a client's board or privacy officer has ruled out any third-party LLM endpoint touching patient data, we self-host Llama 3 on vLLM entirely inside the customer's own cloud account, in whichever region they specify, with zero cross-border inference traffic.