Our Technology Stack
LoMac integrates modern analytics infrastructure with practical commercial application. Our capabilities include:
Advanced Analytics & Modeling
- Machine learning (supervised & unsupervised models)
- Multivariate regression and hierarchical modeling
- Predictive forecasting and time-series analysis
- Causal inference and impact measurement
- Scenario simulation and sensitivity modeling
Data Engineering & Architecture
- Structured and semi-structured data pipelines
- SQL-based relational database systems
- Cloud-based data platforms (Azure SQL, modern data warehouses)
- Data normalization and quality validation frameworks
- Automation workflows and scalable reporting systems
AI & Optimization Frameworks
- Performance diagnostics modeling
- Execution variance detection
- Algorithmic optimization models
- Decision-support dashboards
- KPI-driven performance tracking systems
We combine statistical depth with applied business logic — ensuring that models translate directly into operational and financial outcomes.
Our Methodology
LoMac follows a structured, defensible engagement model designed to move from ambiguity to measurable impact.
Diagnostic Deconstruction
We interrogate the data environment to identify structural inefficiencies, performance variance, and hidden revenue leakage. This includes statistical validation, variance analysis, and root-cause modeling.
Model Construction
We build predictive and diagnostic models tailored to the client’s operating environment. Every model is stress-tested, sensitivity-analyzed, and benchmarked against realistic constraints.
Quantified Strategy Design
Insights are converted into structured strategic actions with measurable KPIs. No recommendations are made without a defined performance metric and ROI logic.
Performance Validation
We establish monitoring frameworks to track lift, efficiency gains, and variance reduction. Strategy is not static — it is continuously measured and optimized.