Systems That
Cannot Fail

The Challenge

A national health insurance system processing millions of claims monthly faced widespread fraud, duplicate submissions, and a manual review process overwhelmed by rapid membership expansion. Adjudication backlogs measured in weeks were undermining provider relationships and program integrity.

The system was simultaneously onboarding providers and members at scale, with existing infrastructure unable to absorb volume increases without significant performance degradation.

Technical Approach

An ensemble ML claims intelligence pipeline scores each incoming claim in real time before adjudication — flagging high-risk submissions based on provider behavior profiles, diagnosis-procedure code relationships, and historical fraud patterns.

An NLP layer identifies duplicate narratives and inconsistent clinical coding. The system integrates via FHIR R4 APIs with the existing claims platform, requiring no system replacement. A compliance dashboard gives officers real-time anomaly visibility by facility, claim type, and region.

The Challenge

A national authority distributing pharmaceuticals and medical equipment to thousands of health facilities operated without demand forecasting. Procurement was driven by historical averages and manual requests — producing chronic stockouts of essential medicines and compounding overstocking of slow-moving items at central warehouses.

Donor reporting requirements from major global funders demanded granular inventory visibility that the existing system could not produce, with reconciliation taking weeks per cycle.

Technical Approach

A time-series demand forecasting model incorporating consumption history, disease burden data, seasonal patterns, and supply lead-time distributions generated facility-level reorder recommendations 8 weeks ahead — enabling procurement before stockouts materialized.

An ETL pipeline aggregates DHIS2 consumption data into a central warehouse, updated daily. A donor reporting layer automates World Bank and Global Fund compliance report generation. Expiry risk alerts enable proactive stock redistribution between facilities.

The Challenge

A global health implementer operating a donor-funded HIV/TB treatment program across 400+ health facilities managed patient records across five incompatible EMR systems — no unified patient view, no real-time dashboard, and no automated mechanism for producing quarterly PEPFAR and Global Fund performance reports.

Hundreds of person-hours per quarter were consumed by manual data reconciliation. Patient transfers created duplicate records and treatment gaps with direct implications for viral suppression outcomes.

Technical Approach

A FHIR R4 interoperability layer standardizes patient data across all five EMR systems without replacing any. A Master Patient Index linked records across facilities using probabilistic matching on demographic and clinical identifiers — resolving duplicates without requiring national IDs.

A central analytics platform processes the unified data stream in near-real-time, surfacing KPIs to program managers and generating PEPFAR MER and Global Fund reports automatically. Varona operates as an embedded technical partner within the implementer's program structure.

The Challenge

A national power utility faced significant non-technical losses (NTL) — electricity theft, meter tampering, and billing anomalies — across a distribution network serving millions of customers. Without granular consumption analytics, high-loss zones were identified only after large financial damage had accumulated.

Maintenance and revenue assurance teams operated reactively. Grid monitoring relied on periodic manual audits, leaving weeks-long visibility gaps. Load forecasting was performed using static seasonal averages with no integration of real-time demand signals or weather data.

Technical Approach

An anomaly detection pipeline integrates smart meter data, billing records, and grid sensor feeds into a unified analytics layer. ML models identify statistical deviations between expected and billed consumption at meter, feeder, and substation level — flagging high-probability theft or tampering for field investigation.

A demand forecasting module incorporates temperature, time-of-day, day-type, and historical load patterns to optimize generation scheduling and reduce costly load-balancing interventions. A real-time operations dashboard surfaces KPIs across the distribution network — enabling proactive response to emerging anomalies before they compound.

The Challenge

Defense and national security operations rely on complex logistics networks to deliver critical resources — fuel, medical supplies, equipment, and personnel — across geographically distributed environments. These systems are often fragmented across multiple agencies and data sources, reliant on manual planning and delayed reporting, and vulnerable to disruption from poor visibility.

In high-stakes environments, delays or misallocation of resources carry direct operational consequences. The inability to detect supply chain anomalies before they compound creates systemic risk that traditional auditing cannot address at the required tempo.

Technical Approach

A modular AI and data infrastructure framework supports logistics intelligence and operational decision-making at scale. A unified data ingestion layer aggregates logistics, inventory, and operational data from multiple systems — enabling near-real-time visibility across distributed environments.

ML models forecast demand for critical resources and identify potential supply chain disruptions before they occur. An anomaly detection layer flags irregular patterns in supply movement and inventory levels, providing early warning of system failures or inefficiencies. A decision support interface delivers dashboards enabling scenario analysis and course-of-action planning under uncertainty.