MDY Analytics

Innovation portfolio

Fourteen analytical innovations across four technology domains, designed as standalone products that share methodological foundations. Every one is the work of the same founder and lead developer, built in continuous succession since 2019, and priced and configured to be within reach of NHS, national-government and multilateral-institution buyers in high-income and lower-middle-income economies.

7 innovations, three of them the components of the PARIS AI suite

Clinical AI and Health Intelligence

Clinical decision-making in acute, primary and post-acute care runs on threshold alarms and reactive monitoring. Threshold systems report that a crisis has arrived; they do not say when one will arrive. Risk scores classify the current state but carry no time dimension. The complex polypharmacy population carries an avoidable adverse-event burden, post-acute patients are readmitted within days of discharge, and prescribing, dispensing and adherence still run across paper and partly digital systems.

Delivers: Software as a medical device, multi-platform clinical applications, wearables-based monitoring and NHS-grade decision support.

YOARLY

The wearable form of the PARIS AI AEP engine

Your Early Warning. Not a tracker or a monitor but a warning system that operates before the crisis: the same engine applied to continuous sensor streams from a wearable device, producing graduated alerts from immediate escalation to confidence over a multi-day horizon.

Relationships
Inherits the PARIS AI AEP engine
Markets
UK and West Africa

DEWLY

Earlier warning of deterioration in critical care

Delivered as software as a medical device, DEWLY detects deterioration earlier than threshold-based monitoring, using the routine observations already collected in intensive care and high-dependency units. It produces three clinically distinct outputs from one analysis: a deterioration warning, a stability confirmation and a recovery forecast. It operates alongside NEWS2 without changing how observations are collected, documented or escalated.

Setting
Intensive care and high-dependency units

PARIS AI Poly Component of PARIS AI

Polypharmacy risk intelligence

A clinical decision-support application for polypharmacy risk assessment, delivered across multiple platforms. Built on regulatory adverse-event evidence, it gives the clinician a single actionable risk tier with a defensible recommendation.

Relationships
PARIS AI suite; shares its adverse-event modelling discipline with RxIndex, UpLabel and MaxGeniz

RxIndex

A population-normalised medication risk index

A continuous, real-world medication risk index for the complex polypharmacy population: patients on five or more medications with three or more long-term conditions. It produces a comparative risk score for every medication in the FDA database from regulatory and real-world adverse-event evidence, delivered as interactive clinical dashboards and structured data exports.

Relationships
Shares its adverse-event modelling discipline with PARIS AI Poly, UpLabel and MaxGeniz

SMedRx3

Digital prescription, dispensation and adherence

Each prescription is tamper-evident and tracked from prescribing through dispensing to administration, with role-based access scoped to clinical responsibility, a full audit trail, and offline-first operation with cloud synchronisation. It addresses prescription integrity, adherence visibility and cross-facility continuity, and interoperates with electronic health records, billing and pharmacy systems where integration is implemented.

Market
NHS and community pharmacy

PARIS-Ai Rehab Component of PARIS AI

Rehabilitation trajectory monitoring for post-acute care

Tracks functional recovery across the rehabilitation journey, predicts recovery trajectories, identifies setbacks before they occur and optimises the timing of intervention across eleven rehabilitation pathways, from post-stroke and cardiac to post-surgical and complex medical. Recovery is tracked separately across five functional domains rather than collapsed into a single index. Delivered across multiple platforms with end-to-end encryption.

Relationships
PARIS AI suite; designed to integrate with PARIS AI AEP, PARIS AI Poly and SMedRx3 in the clinical workflow
2 innovations

Pharmacovigilance and Drug Safety

Post-market drug safety depends on three evidence streams that are rarely brought together: official drug labels, real-world adverse-event reporting databases, and predictive intelligence. Labels reflect the regulatory baseline at approval and lag real-world signal. Spontaneous reporting systems aggregate reports but offer no predictive horizon. The complex polypharmacy population is excluded from the randomised trials whose results populate labels and guidance, so the evidence gap for these patients is structural.

Delivers: Regulator-ready gap analyses and real-world evidence at guideline standard.

UpLabel

Post-market drug surveillance for regulators

A disease-agnostic platform that compares official drug-label content against statistical and predictive evidence derived from real-world adverse-event data, and produces structured gap analyses between the label, real-world evidence and prediction. It operates on data already held in regulatory reporting systems with no new clinical infrastructure or patient contact, and produces outputs structured for FDA, MHRA, EMA and NICE submission.

Relationships
Pharmacovigilance pair with MaxGeniz

MaxGeniz

Real-world evidence for the complex polypharmacy population

An integrated data-science platform for patients with complex polypharmacy across cardiovascular disease, type 2 diabetes, mental health, cancer and obesity: the most clinically complex, most resource-intensive and least represented in the trials that inform guidance. A discovery phase characterises the population, maps medication patterns and produces hypotheses ready for confirmatory study; an evidence phase turns them into NICE-compliant real-world evidence through multi-site NHS analysis without centralising patient data, yielding causal estimates at guideline standard.

Relationships
Pharmacovigilance pair with UpLabel
Markets
NHS trusts, enterprise, MENA
2 innovations

Financial Intelligence and Forensics

Forensic financial detection runs on single-method tools, and none of them converges several independent detection methods into one ranked output. Due diligence on a single entity typically requires separately procured capabilities for financial-statement analysis, forensic detection, tax assessment and media monitoring, each with its own data model and output, synthesised by hand at the end.

Delivers: Ranked forensic risk and single-entity due diligence at scale.

REVEDA Revenue Evasion, Changepoint and Anomaly Detection

The forensic detection engine

REVEDA applies several independent detection methods to financial data at once and converges the outputs into the CAF Index (Changepoints, Anomalies, Fraud), a single composite forensic risk score that ranks every entity by aggregate detection signal, supporting evidence-based audit selection and regulatory escalation. It operates as a standalone tool and as the engine embedded within CREDA.

Relationships
Embedded in CREDA under sub-licence

CREDA

Integrated due-diligence and compliance analytics

CREDA combines company financial reports, global tax intelligence, REVEDA forensic analytics and media signals into a single compliance and risk picture for each entity. It computes financial performance, risk and compliance indicators across six proprietary metric frameworks, assesses tax liability with live global rate referencing by country, year and industry, embeds the full REVEDA engine and CAF Index, forecasts financial variables and surfaces reputational signals from news and social media. The result is the Aggregate Risk Score, one ranked composite per entity.

Relationships
Embeds REVEDA
Market
Due diligence, compliance and tax
3 innovations

Economic and Fiscal Intelligence

Economic and fiscal tooling for finance ministries, central banks, development banks and regional economic communities is underserved at the price point these institutions can reach, particularly in lower-middle-income economies. Existing platforms provide trend lines, growth rates and benchmarks, but no formal, reproducible measure of how a fiscal indicator, economic series or commodity trade position has structurally evolved over a defined period.

Delivers: Three platforms sharing the MDY Structural Evolution Index, MDY[SEI], as their common analytical signature.

FLOWZZ

Fiscal intelligence on IMF Government Finance Statistics

FLOWZZ takes the analyst through three progressive phases: discovery, with comparative aggregates, a hierarchical explorer of the GFS classification, multi-country time series and relative change analytics; dynamics, with moving and rolling statistics filtered by income class and geography; and decision intelligence, with fiscal forecasting, simulation and surface visualisation that identify which fiscal levers most strongly drive a target outcome.

Relationships
Economic intelligence trio with KERD and MERD via MDY[SEI]
Buyers
Fiscal analysts and policymakers

KERD

Multi-domain economic intelligence

KERD integrates World Bank development indicators, IMF government finance and international financial statistics, WTO trade datasets and international price databases into one analytical environment. Six modules share a unified architecture: macroeconomic analytics with keyword discovery and before-and-after intervention analysis; fiscal and debt time-series diagnostics and forecasting; infrastructure investment and financing analytics; merchandise, services and digital trade with comparative-advantage measures; commodity prices, price indices, exchange rates and monetary data; and price index forecasting and optimisation.

Relationships
Economic intelligence trio with FLOWZZ and MERD via MDY[SEI]
Buyers
Economists, policymakers and multilateral development banks

MERD

Agricultural-trade structural evolution on FAOSTAT

An analytical platform for food and agricultural commodity trade policy built on the full FAO FAOSTAT dataset. At its core is MDY[SEI], the MDY Structural Evolution Index, a formal and reproducible measure of how an agricultural product's position within an economy has evolved over a defined horizon: for any FAO-tracked product and country it returns a structural-evolution score, a rank, a five-tier classification of change significance and boundary statistics. Seven analytical modules surround it, refreshed monthly against FAOSTAT and UN COMTRADE.

Relationships
Origin of MDY[SEI], reused under licence in FLOWZZ and KERD
Users
Ministries of agriculture and trade, the African Development Bank, FAO, the World Bank, IFAD, ECOWAS and ASEAN

In the App Store, and behind the portfolio

Applications available today, and the expertise that builds the innovations above.

HealthWalletQR

iOS app

A personal health passport that works without a connection. Health data stays on the device and can be shared instantly by QR code.

App Store

Fiscal Explorer

iOS app

Navigate IMF Government Finance Statistics with classification search, multi-dimensional filtering and year-based groupings, built on the GFS 2014 methodology.

App Store

MERD analysis app series

iOS app series

Agricultural analytics across the value chain, from inputs to trade and food security, with statistical and machine learning tools that work offline.

Expert services

Consultancy and development

Data science, statistical modelling, health technology validation, and iOS and macOS development, from proof of concept to deployed system.

Speak with our experts

Working with the portfolio

Clinical partners, regulators, finance ministries and development institutions each engage with a different part of the portfolio. Tell us which, and we will send the relevant material under NDA.