Most RAG failures aren't model problems — they're retrieval problems. Here's our production checklist for building reliable retrieval pipelines that actually work at scale. We break down the five most common failure modes and show you how to diagnose each one.
A camera is just a sensor. When does the cost of a CV pipeline outweigh a $5 IoT sensor? We present a decision framework that accounts for accuracy requirements, latency budgets, and long-term maintenance costs.
Infrastructure, data labeling, and iteration cycles are the hidden 80%. How to budget for AI projects that actually ship. We walk through real project budgets and show where the money really goes.
Moving a model from a Jupyter notebook to a factory-floor device is where most teams fail. Here's the playbook we use for sub-50ms inference on constrained hardware — covering quantization, runtime selection, and monitoring in production.
Not all augmentations are created equal. We benchmarked 15 strategies on real manufacturing datasets — here's what worked and what was noise. Includes results tables and our recommended pipeline configuration.
When should you use an API and when should you train your own model? We break down the economics, latency, and control trade-offs. A practical guide for technical founders making infrastructure bets.