Thought Leadership
Blog
Applied ML tutorials, NLP use cases in finance and government, and practical insights from building real production systems.
Text-only pipelines miss half the information in most real-world documents. Here's why multimodal embeddings — and Google Gemini specifically — change the equation.
The model works in your notebook. Now what? A practical checklist for deploying ML models — based on what we've actually shipped, not what the textbooks recommend.
When Your Client Has 500 Rows, Not 500 Million
Most real-world ML happens on small datasets. Here's how to make it work — feature engineering, strong priors, validation strategy, and knowing when to stop.
You don't need Databricks, Snowflake, or a 10-person data team to get value from ML. Here's the practical stack we use for small-business deployments — and what you can skip.
A beach resort and an urgent care network face the same core problem: demand that swings hard by season. Here's how we built forecasting systems for both — and why Prophet was only the starting point.
Intake forms, medical records, and police reports — all in two languages. How we built an NLP-powered case triage system for a South Texas personal injury firm.
We built ML pricing models for a boat dealer and a used-car lot. The industries are different. The models are surprisingly similar — and the real gains came from feature engineering, not algorithms.
10 ML Pipelines for 10 Different Industries: What We Learned
We built ML systems for boat dealers, shrimp fleets, law firms, hospitals, and more. Here's what actually transfers across industries — and what doesn't.