Mizan · Pitch Deck
Healthcare · POPULATION HEALTH
Mizanميزان
Risk-stratified population health, benchmarked against Saudi outcomes.
neuriya.ai — Sovereign, Arabic-first enterprise AI
Problem
Primary care networks find out who's at risk when the patient shows up in the ER
- Diabetes, cardiovascular, and obesity risk is usually assessed patient by patient at the point of visit, with no population-level view of who is trending toward complications.
- Risk models used across the region are often calibrated on imported outcome data, not on how these conditions actually progress in a Saudi population.
- Overdue screenings, labs, and follow-ups go undetected until a patient falls far enough out of guideline to notice on their own or arrive as an emergency.
- Outreach to at-risk patients is tracked informally — a phone call log or a spreadsheet note — so it's rarely clear whether a flagged gap was ever actually closed.
Solution
Every enrolled patient scored, every care gap tracked to close, not just to flag
Product
Mizan in action
How it works
From first touch to outcome
Stratify
Every enrolled patient is scored against Saudi-benchmarked risk models for diabetes, cardiovascular disease, and obesity.
Detect
Open care gaps — overdue screenings, labs, follow-ups — are identified automatically across the population.
Reach out
Outreach is tracked from first contact through appointment booking, so nothing falls silently out of the pipeline.
Close
Completed screenings and visits close the gap and feed back into the cohort’s trended risk score.
Market
Sizing the opportunity
- TAM — $800M. MENA population-health-management market
- SAM — $95M. Saudi primary-care risk-stratification & chronic-disease-management software market
- SOM — $7M. 3-year obtainable share via design-partner primary care networks
Nationally rising diabetes and cardiovascular prevalence, combined with Vision 2030's shift toward value-based primary care, is turning population-level risk stratification from a research exercise into an operational requirement for primary care networks.
Business model
How Mizan makes money
Per-enrolled-patient subscription
Recurring fee priced per patient actively enrolled and risk-stratified within the network.
Per-network platform license
Recurring fee covering the cohort dashboard, care-gap workflow, and provider-assignment tooling for the whole network.
Implementation & data-integration fee
One-time fee to connect EMR and claims data and calibrate risk models to the network's enrolled population.
Revenue streams
Where the revenue actually comes from
Competition
Where we win
Our edge
- Risk models benchmarked against Saudi-specific outcome data, not imported norms retrofitted to a local population.
- A closed-loop outreach workflow that tracks a gap through to closure, not a dashboard that ends at identification.
- Provider-level accountability built into the workflow — every high-risk patient has a named owner, not just a flag.
- Founding team pairs Saudi enterprise delivery experience with applied population-health data science.
Traction
A complete cohort platform, seeking its first network
- The population health product is built end-to-end: population dashboard, cohort registry, care-gap workflow, and patient risk profile, not a static reporting deck.
- Architecture pre-aligned to PDPL, NDMO Classification, MoH Standards, HL7 FHIR R4, CCHI, and ISO 27001.
- Actively selecting design-partner primary care networks for a first population health pilot cohort.
Go-to-market
One cohort first, full enrolled population once gaps start closing
- 1Land with 1-2 design-partner primary care networks, starting with a single high-prevalence cohort such as diabetes.
- 2Prove care-gap closure rate and outreach conversion over a pilot period against the network's existing manual process.
- 3Expand to additional cohorts and the network's full enrolled population, then through payer relationships to reach covered members at scale.
Team
Built by operators, not just builders
The ask
Raising a seed round to enroll the first at-risk cohort
We're raising to fund Saudi-outcome model calibration, EMR/claims integration work, and the core data-science and engineering team through first-revenue pilot deployments.