Productionized Case Studies
This page presents selected case studies covering production-grade ML and LLM systems designed and implemented under real enterprise constraints. Each case study focuses on architectural decisions, data and model trade-offs, and operational considerations such as cost control, governance, scalability, and reliability.
Case Study 1: LLM-Augmented Weekly Business Review (WBR) System
Objective
Design and deploy a scalable, cost-controlled LLM-augmented analytics system to automate weekly and monthly executive business reviews, replacing manual, error-prone analysis workflows used by category managers and senior leadership.
The system standardizes narrative insights across multiple business dimensions while operating on TB-scale transactional data, with strict requirements around interpretability, reproducibility, and budget governance.
What this case study covers
- Designing an OLAP-first analytics pipeline for high-volume, multi-dimensional reporting
- Feature engineering under enterprise reporting and data-consistency constraints
- LLM selection, prompt design, and cost/latency trade-offs at scale
- FastAPI-based service separation between analytics, application logic, and AI inference
- Governance mechanisms including caching, rate limiting, and budget controls for sustained LLM usage
Design Philosophy
This system was intentionally built as a batch-oriented, enterprise-grade analytics platform, prioritizing consistency, explainability, and operational reliability over real-time inference. The LLM augments human decision-making rather than replacing core BI or financial controls.
Case Study 2: Geospatial ML–Driven New Store Site Selection & Sales Forecasting
Objective
Design and deploy a production-grade geospatial machine learning system to support New-to-Industry (NTI) retail site selection for a $10B+ convenience and prepared foods retailer (2,500+ locations).
The goal was to replace decentralized, intuition-driven real estate decisions with a standardized, explainable, and scalable forecasting engine capable of estimating 3-year category-wise sales from a simple latitude/longitude input.
The system directly informs multi-million dollar CAPEX decisions, where errors translate into long-term lease risk and irreversible investment commitments.
What this case study covers
- Large-scale geospatial feature engineering across census, mobility, traffic, and infrastructure datasets using Spark on Databricks
- Designing a dual trade-area framework (drive-time isochrones and radial distances) to model real-world accessibility and customer behavior
- Cold-start modeling strategies for NTI locations using clustering-based “statistical twins”
- Multi-vertical XGBoost forecasting architecture (fuel, diesel, prepared food, grocery)
- Explainability-first ML design using SHAP to support executive trust and auditability
- Production deployment using MLflow, Delta Lake, and serverless model serving, with cost and drift governance
Design Philosophy
This system was intentionally built as a spatial-first, cross-sectional modeling platform, prioritizing where a store is over when it opens. Architectural choices favored interpretability, reproducibility, and operational scalability over purely academic modeling approaches, ensuring the solution could be trusted and adopted by real estate and finance leadership at enterprise scale.
(More case studies will be added here, covering ML platforms, LLM pipelines, and large-scale data engineering systems.)