Research & Perspectives
Insights
Practical perspectives on AI, machine learning, and data strategy from our team. No hype—just what works.
Why Most Enterprise AI Projects Fail—And How to Beat the Odds
Research shows 85% of AI initiatives never reach production. After deploying models across defense, healthcare, and government, we've identified the patterns that separate successful projects from expensive experiments.
Transformers Explained: Why Attention Changed Everything
The attention mechanism behind GPT, BERT, and modern AI isn't magic—it's elegant math. We break down how self-attention lets models understand context in ways previous architectures couldn't.
The Hidden Cost of Model Drift
Your ML model worked great in testing. Six months later, accuracy dropped 23%. Model drift is the silent killer of production AI—here's how to detect and prevent it.
Feature Stores: The Infrastructure You're Missing
Why are you recomputing the same features for every model? Feature stores solve the consistency and duplication problem that plagues most ML pipelines.
RAG vs. Fine-Tuning: Choosing the Right Approach
Not every LLM use case needs fine-tuning. Retrieval-augmented generation often delivers better results with less risk. Here's our decision framework for enterprise deployments.
ML in Classified Environments: Constraints Drive Innovation
Air-gapped networks. No cloud. Limited compute. Working in SCIFs forces architectural decisions that often produce more robust solutions than unlimited-resource approaches.
Convolutional Neural Networks: Still Relevant in 2025
Transformers get the headlines, but CNNs remain the workhorse for computer vision in production. When latency and compute matter, convolutions still win.