Industry AI

AI Healthcare Solutions

The most valuable healthcare AI right now is not diagnostic. It is administrative — the documentation burden that consumes clinician time and produces no care.

Engagements from $10,000 to $100,000+Serving USA · UAE · UK · Canada · EuropeYou own the code and the IP
The problem

Administrative burden is the addressable problem

And it is where AI can help without entering regulated clinical decision-making.

Clinicians spend a substantial share of their working day on documentation, prior authorisation and correspondence. Automating that produces measurable time back without touching diagnosis or treatment recommendation — which carry medical device regulation in most jurisdictions.

We are deliberate about the line. Systems that summarise a consultation for clinician review, extract fields from a referral letter, or draft a prior authorisation are administrative. Systems that suggest a diagnosis or a treatment are clinical decision support, and they attract regulatory obligations that must be assessed before a line of code is written.

  • Administrative automation, clearly scoped away from diagnosis
  • Clinician review on every generated clinical note
  • Minimum-necessary PHI access enforced technically
  • BAA-covered vendor services where PHI is processed
  • De-identification where the use case permits it
  • Full access audit trail for every PHI touch
  • Clinical decision support scoped only with regulatory assessment
Use cases

Healthcare AI use cases

Clinical documentation support

Consultation notes drafted from dictation or transcript for clinician review and sign-off.

Patient intake and triage

Structured history collection and urgency triage against your protocols before the appointment.

Prior authorisation

Payer requirements interpreted and submission packs assembled from the clinical record.

Referral letter processing

Incoming referrals extracted, categorised and routed to the right service.

Medical coding support

Code suggestions with supporting evidence from the record, for coder review.

Patient communication

Appointment reminders, preparation instructions and follow-up in the patient's language.

Records summarisation

Long histories condensed for handover, with source references retained.

Denial management

Denial reasons interpreted and appeal documentation drafted.

Workflow

Example workflow — referral intake

01 Referral receivedBy fax, email, portal or HL7 interface in whatever format the sender used.
02 ExtractionPatient details, referring clinician, reason and clinical history extracted with confidence scores.
03 ValidationPatient matched against the record system; duplicates and mismatches flagged.
04 TriageUrgency assessed against your documented protocols, never against a model's clinical opinion.
05 RoutingDirected to the correct service and waiting list with the summary attached.
06 AcknowledgementReferrer and patient notified automatically.
07 Review queueAnything uncertain or urgent flagged for immediate human attention.
Integrations

Systems and compliance considerations

Ezulix builds compliance-ready architecture designed to support HIPAA and GDPR requirements. Certification status should be confirmed with our team. [VERIFY]

  • EHR and EMR systems via HL7 v2, FHIR or vendor APIs
  • Practice management and scheduling systems
  • Radiology, laboratory and diagnostic systems
  • Payer and clearinghouse interfaces
  • Patient portals and communication channels
  • Deployment in BAA-covered cloud regions
  • Audit and access logging aligned to HIPAA expectations
How we deliver

Ten stages from first call to a system your team trusts

Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.

Discovery

A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.

AI Opportunity Assessment

We score candidate use cases on data readiness, volume, error tolerance and payback, then rank them. Some come back "do not use AI for this" — you get that answer too.

Solution Architecture

Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.

Proof of Concept

A narrow build against your real data to prove accuracy on the cases that matter, typically 2–4 weeks. Go / no-go decision at the end.

MVP

One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.

Production Development

Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.

Integration

Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.

Security Testing

Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.

Deployment

Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.

Monitoring & Optimization

Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.

Security & Governance

Security-conscious architecture, from the first design review

Enterprise AI fails on governance more often than on models. Every system we build is designed to support enterprise security requirements and to give your risk team answers rather than assurances.

Data privacy & residency

Your data stays in the region and tenancy you nominate. We architect for no-training-on-your-data configurations and document exactly which vendor endpoints see which fields.

Role-based access control

Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.

Authentication & authorization

SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.

Encryption

TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.

API security

Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.

Audit logging

Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.

Data isolation

Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.

Secure prompt handling

System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.

PII protection

Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.

Human approval workflows

High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.

Monitoring & anomaly detection

Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.

Rate limiting & abuse control

Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.

Secure deployment

Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.

On compliance: Ezulix designs compliance-ready architecture aligned to frameworks such as GDPR, HIPAA and SOC 2 control objectives. Certification status for any specific standard should be confirmed directly with our team before contract. [VERIFY: current Ezulix certifications]
Reference builds

The kind of systems we are asked to build

Representative scopes drawn from the categories Ezulix works in. Client names and outcome figures are withheld until verified.

Customer Support · SaaS

AI Customer Support Platform

Tier-1 ticket deflection using RAG over product documentation and past resolved tickets, with confidence-gated handoff to human agents and full conversation audit.

[CASE STUDY METRIC]Deflection rate
[PROJECT RESULT]First-response time
Revenue · B2B

AI Sales Agent

An agent that qualifies inbound leads against ICP criteria, enriches company data, writes a researched first-touch email and books directly into rep calendars.

[CASE STUDY METRIC]Speed to lead
[PROJECT RESULT]Meetings booked
Knowledge · Enterprise

Enterprise RAG Knowledge Assistant

Permission-aware assistant over SharePoint, Confluence and a contract repository, with hybrid retrieval, re-ranking and mandatory source citation on every answer.

[CASE STUDY METRIC]Search time saved
[PROJECT RESULT]Answer accuracy
Illustrative scopes. These are hypothetical/demo project shapes, not published client work. Metrics are placeholders — replace [CASE STUDY METRIC] and [PROJECT RESULT] with signed-off figures, and add [CLIENT NAME] only where you hold written permission.
FAQ

Questions enterprise buyers ask us first

Can healthcare AI be HIPAA compliant?
A system can be built to support HIPAA requirements — BAA-covered infrastructure and model services, minimum-necessary access, encryption, audit logging and contractual prohibition on training with PHI. Compliance is a property of your organisation and its agreements, not a certificate a vendor can hand you. We build the technical controls and provide documentation; your privacy officer and counsel assess compliance.
Does the AI make clinical decisions?
No. We scope to administrative and documentation support with clinician review on anything clinical. Systems that recommend diagnosis or treatment may be regulated as medical devices in the US, EU and elsewhere, and that assessment must precede any such project.
Can it work with our EHR?
Yes, through FHIR, HL7 v2 or vendor APIs depending on your system and version. Integration effort varies considerably by vendor, and we scope it specifically rather than assuming.
Is patient data used to improve the model?
No. We use enterprise endpoints with training disabled and contractual guarantees, or self-hosted models inside your boundary. Which fields reach which endpoint is documented explicitly for your privacy review.
Project brief

Talk to an AI solution architect

No junior sales rep, no discovery deck. The person on the call is the person who will design the system.

  • Response within one business day
  • Mutual NDA signed before detailed discussion
  • Written scope, one price, one delivery date
  • You own all source code, models and IP at launch
Email: sales@ezulix.com [VERIFY]

We use these details only to prepare your scope and estimate. Your idea stays yours — mutual NDA before any detailed discussion.

Next step

Bring us the process that is costing you the most.

Book a 45-minute call with a solution architect. You leave with a use-case shortlist, a reference architecture sketch and a realistic build envelope — whether or not you build it with Ezulix.