AI-powered real-time risk prediction for operating rooms and ICUs. Software layer on your existing monitors — no new hardware.
An anaesthetist monitors 15+ physiological parameters simultaneously. Heart rate drifts. Blood pressure softens. SpO₂ ticks down. Each signal alone — unremarkable. Together, they form a pattern that a tired human brain, four hours into a case, may not catch in time.
These risks are interconnected — hypotension drives AKI, hypoxaemia escalates into respiratory failure. A unified system tracking the full patient journey catches cascading risks that siloed monitors miss.
This isn’t replacing clinical judgment — it’s giving clinicians time they don’t currently have.
Patient history, labs, comorbidities, ASA classification, procedure type — ingested from your EHR via HL7/FHIR.
BP, ECG, SpO₂, EtCO₂, temperature, ventilation parameters — streamed continuously from existing monitors.
Ensemble models trained on 10,000+ surgical cases generate rolling probability estimates for key adverse events.
When risk exceeds clinical thresholds: alert with ranked contributing factors. The clinician sees why, not just what.
| Adverse Event | Prediction Window | Status | Clinical Significance |
|---|---|---|---|
| Intra-op Hypotension | 10 min ahead | Live | MAP <65 mmHg linked to myocardial injury, AKI, stroke |
| Intra-op Hypoxaemia | 10 min ahead | Live | Desaturation cascade; reintubation risk; prolonged ventilation |
| Acute Kidney Injury | 6 hrs ahead | Live | 7% incidence, 25% mortality; early fluid mgmt cuts progression |
| Sepsis | 4–8 hrs ahead | In Dev | Each hour of delayed antibiotics increases mortality up to 7% |
| Respiratory Failure | Hours ahead | In Dev | Unplanned reintubation: 6–10× mortality increase |
Current models: >90% accuracy on VitalDB and MIMIC-IV datasets.
Every deployment trains on site-specific data — surgical mix, patient population, local protocols. Accuracy improves with each hospital, creating a compounding data advantage no competitor can replicate.
We analyse real-time capnography waveforms captured in-hospital — rich data not in any public dataset. No one else is using these curves for predictive modelling.
From induction in OR to ICU discharge. Every competitor addresses a single parameter or a single phase. We cover intra-op and post-op adverse events in one unified system.
Oxford Anaesthesia, Stanford AI/Anesthesia, Imperial, UCLH — our advisors shape our models and open clinical partnerships.
Partner clinics today — customers tomorrow.
Observational Phase 1 pilot study to begin on-site. Access to intra-op data including capnography waveforms.
Target: 3-6 hospital sites for multi-site clinical validation study.
Regulatory pathway. FDA 510(k) for Class II CDS (predicate: Edwards HPI). Parallel MHRA Digital Health Technology route. Target market entry Q2–Q3 2028.
Global expansion. Focus markets: United Kingdom and United States. Planned extension to Nigeria and South Africa via advisors and partners.
Automated, structured post-operative reports from anaesthesia monitoring data. Every surgery, fully documented — review and sign off, no manual writing.
Hypotension time, episodes, vasopressor log
Desaturations, ventilation changes, airway events
Crystalloid/colloid, EBL, transfusion events
All agents with doses, routes, timestamps
Duration, anaesthesia time, ASA, complexity
Prose summary — review & sign off, not write
Structured data for quality reporting
Accurate OPCS-4 / ICD-10 coding
Queryable data for training & research
Case data for analytics & costing
2 yrs in fintech, launched 4× offices
MSc Pharmacology, University of Oxford
MSc Biomedical Engineering, TUM
BSc Biological & Medical Physics, MIPT
NHS Doctor in Neurosurgery (practicing)
MRes Surgical Sciences, University of Oxford
1× unicorn co-founder (Uzum)
4× product founder
Co-Founder & COO, Roumai Medical
Professor of Anaesthesia, University of Oxford
Professor of Anesthesia & Data Science, Stanford University
Anaesthetist, University College London Hospitals
Clinical Scientist and Surgeon, Imperial College Healthcare
Professor of Anaesthetics, University of Oxford
Chair of Clinical Machine Learning, University of Oxford
Health Systems researcher, University of Oxford
Anaesthetist NHS, CEO & Director of Multiple Companies
Every surgical team has a moment where they wish they’d known
five minutes sooner.
We’re building the system that gives them that time.
Interested in partnering, investing, or learning more? Reach out to our team.