Yaqaza · Pitch Deck
Healthcare · CLINICAL DECISION SUPPORT
Yaqazaيقظة
Predictive risk, ahead of the code blue.
neuriya.ai — Sovereign, Arabic-first enterprise AI
Problem
Deterioration is spotted by a nurse's instinct, not a continuous score
- Sepsis and deterioration are typically caught when a nurse notices something at a scheduled vitals round, not continuously between rounds.
- Ward-wide risk trends are invisible until a patient is already coding — there is no single view of who is drifting toward rapid response.
- Bed-day and length-of-stay planning is done from historical averages and spreadsheet guesswork, not a per-patient forecast.
- Early-warning thresholds are mostly imported from Western risk-scoring models, not recalibrated against how Saudi inpatients actually present.
Solution
A continuously recalculated risk score, calibrated on Saudi inpatients
Product
Yaqaza in action
How it works
From first touch to outcome
Ingest
Vitals, labs, and flowsheet data stream in continuously over FHIR R4 from the ward EMR.
Score
Risk models recompute sepsis, deterioration, and LOS scores in near real time.
Alert
Patients crossing risk thresholds surface on the ward dashboard and route to the covering clinician.
Respond
The care team acts, and the outcome feeds back into model calibration.
Market
Sizing the opportunity
- TAM — $1.1B. MENA hospital clinical decision-support & predictive analytics market
- SAM — $150M. Saudi hospital risk-stratification & early-warning software market
- SOM — $9M. 3-year obtainable share via design-partner hospital pilots
Saudi hospitals are adding beds under Vision 2030's capacity expansion faster than they can add specialist nursing coverage, and MoH's push to modernize inpatient care puts continuous, data-driven deterioration monitoring on the hospital-cluster agenda rather than treating it as a boutique ICU tool.
Business model
How Yaqaza makes money
Per-bed annual license
Priced per licensed bed under active monitoring, tiered by ward acuity mix.
Per-hospital platform subscription
Recurring fee covering the ward dashboard, alerting, and length-of-stay forecasting modules for the whole facility.
Implementation & calibration fee
One-time fee to connect FHIR feeds and recalibrate risk thresholds against the hospital's own case mix.
Revenue streams
Where the revenue actually comes from
Competition
Where we win
Our edge
- Sepsis and deterioration models trained and recalibrated on Saudi inpatient cohorts, not imported thresholds from another population.
- Ward-wide predictive heatmap, not a single-patient score — clinicians see who is trending toward rapid response across the whole ward.
- FHIR-native ingestion connects to existing HIS/EMR vitals, labs, and flowsheets without a custom interface build.
- Founding team combines Saudi hospital-delivery experience with hands-on clinical-AI engineering.
Traction
Product-complete and clinically ready to calibrate
- The ward risk product is built end-to-end, not a mockup: risk dashboard, patient watchlist, alert workflow, and case file all shipped.
- Architecture pre-aligned to PDPL, NDMO Classification, MoH Standards, HL7 FHIR R4, NPHIES, and ISO 27001, so a design partner's IT and compliance review starts from a head start, not a blank page.
- Actively selecting design-partner hospitals for a first clinical AI pilot cohort.
Go-to-market
One ward first, hospital-wide once code-blue rates move
- 1Land with 1-2 design-partner hospitals, starting in a single high-acuity ward willing to validate scores against their own case mix.
- 2Prove reductions in unplanned rapid-response events and improved LOS forecast accuracy over a defined pilot period.
- 3Expand ward by ward within the hospital, then across a cluster operator's facilities through MoH and cluster-level relationships.
Team
Built by operators, not just builders
The ask
Raising a seed round to get the risk score into its first ward
We're raising to fund clinical model validation with design-partner hospitals, FHIR/EMR integration work, and the core clinical data-science and engineering team through first-revenue.