Industries

AI for Healthcare

AIYatch builds AI for healthcare providers and health-tech companies: intake and triage assistants, clinical document automation, medical imaging models and capacity forecasting. Every system is designed privacy-first, with audit trails, human sign-off on clinical decisions and deployment options that keep patient data inside your own cloud or on-premise environment.

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Typical challenges

Clinicians and admin staff lose hours to documentation, coding and referral paperwork.
Patient data must stay compliant with HIPAA, GDPR or local health regulations, which rules out many off-the-shelf AI tools.
Appointment no-shows and unpredictable demand make staffing and capacity planning difficult.
Knowledge is scattered across EHRs, PDFs, protocols and emails, so answers are slow to find.

Typical use cases

Patient intake and triage assistants

Chat and voice assistants that collect symptoms, insurance details and history, answer FAQs and route urgent cases to staff, with escalation rules agreed with clinicians.

Clinical documentation and coding support

Speech-to-text plus LLM summarisation drafts visit notes, discharge summaries and suggested codes for a clinician to review and approve.

Medical imaging and diagnostics support

Computer-vision models that flag anomalies in X-rays, scans or dermatology images to prioritise radiologist attention, never to replace it.

Protocol and policy knowledge base

A RAG assistant over clinical guidelines, formularies and internal policies that answers with citations, so staff can verify every response.

Capacity and no-show forecasting

Time-series and classification models that predict demand by department and flag high-risk no-shows for reminder outreach.

Claims and prior-authorisation automation

Document AI that extracts data from referrals, claims and payer correspondence and pre-fills the forms your billing team currently completes by hand.

Questions from this sector

Yes. We can deploy open-source models on your cloud tenancy or on-premise GPUs so no protected health information leaves your environment. When a hosted model is the better choice, we use enterprise API tiers with no-training terms and a signed data-processing agreement, and we document the full data flow before anything ships.

No. We design healthcare systems as decision support: they draft, flag, summarise and prioritise, and a qualified person reviews and approves. Escalation paths and confidence thresholds are agreed with your clinical leads during discovery, tested before launch and monitored afterwards, so the system stays within the role you defined for it.

A discovery sprint takes two to three weeks. A first production use case such as an intake assistant or documentation helper typically ships in six to twelve weeks, longer where integration with an EHR, procurement reviews or validation studies are required. We phase delivery so a supervised pilot with real staff runs before wider rollout.