MOVO-X
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AI is load-bearing infrastructure inside MOVO-X — not a marketing layer. Triage, intake, prediction, and clinical decision-support all run on production AI today.
Most "AI clinic software" is a wrapper around GPT prompts. That doesn't survive a clinical-operations audit. Real clinical AI needs documented model governance, privacy-preserving training, federated-learning paths for sovereign deployments, and human-in-the-loop overrides at every decision point. We built MOVO-X with all of that from day one.
Acuity-based triage at registration, trained per-clinic on historical clinical context. Models are auditable, reproducible, and operate as decision support — never replacing clinician judgment.
Per-patient no-show probability driving WhatsApp reminder timing, overbooking logic, and revenue forecasting. Cuts no-show rate by 40% in production deployments.
Natural-language intake captures symptoms in the patient's own words, mapped to ICD-10 / SNOMED-CT clinical coding. Multi-language, locally-tuned per market.
Face match against captured ID document image. Spoof detection, liveness check. Runs entirely on-device for sovereign deployments.
Per-doctor and per-specialty demand prediction driving staffing, room allocation, and supply ordering. Integrates with HR / inventory systems.
For institutional and sovereign deployments, models train across clinic boundaries without raw data leaving the institution. Differential-privacy guarantees on aggregate signal extraction.
70% faster patient check-in (45 min → 30 sec)
40% reduction in no-show rates with AI-tuned reminders
3× front-desk capacity per FTE
Audit-grade model governance — every AI feature documented for clinical-operations review
MOVO-X AI runs on a small, deliberately-chosen stack: per-clinic models for tasks where clinic-specific tuning matters, multilingual transformer baselines for tasks where general performance is required, and on-prem deployment for jurisdictions that mandate it. Every model has documented purpose, data lineage, evaluation harness, and a deprecation path. We follow the OECD AI Principles, are forward-aligned with the EU AI Act high-risk-AI requirements, and publish methodology where joint research with academic medical centres permits.
No. The AI features are individually-purposed models — triage scoring, no-show prediction, NLP intake — each trained, evaluated, and deployed for its specific task. We use LLMs where they fit (NLP intake, summarisation), but never as a single brain that does everything.
No. Every AI decision is decision-support with a clinician-in-the-loop override. The platform makes recommendations; the clinician decides. Audit logs capture both the AI signal and the clinician's action.
Per-clinic models train on that clinic's anonymised data only. Cross-clinic aggregate patterns are extracted via federated learning with differential-privacy guarantees. Raw patient data never crosses institutional boundaries.
Architecturally forward-compatible with EU AI Act high-risk-AI requirements. Each AI feature has documented risk classification, model cards, and human-oversight controls. We are tracking the EU AI Act implementing regulations and updating posture accordingly.
Yes — for institutional and sovereign deployments, on-prem and federated configurations are available so models train across organisational boundaries without raw data crossing them.
Tell us about your operation. We'll send a tailored proposal — architecture, integration scope, deployment timeline, and total investment — within hours.