Blog
No hype. No vendor fluff. Just lessons from building, measuring, and shipping AI systems that prove they work.
A deployed approval layer was quietly destroying value. From frozen inference caches alone — zero new compute — counterfactual replay proved approved decisions earned −0.175%/trade while rejected ones would have earned +0.151%. Here's the full methodology.
We ran a True-RAG ablation on a production market-analysis assistant. Removing retrieval dropped directional accuracy 2.7 points and degraded calibration (ECE 0.306→0.335). Here's why the default should be RAG — and when fine-tuning earns its place.
MIT reports 95% of generative-AI pilots deliver no measurable P&L impact. We built a churn pilot the way teams actually build it — and our diagnostic proved its headline metrics were phantom before anyone funded a rollout. Here's the 12-check autopsy.
Most AI governance lives in PDFs nobody reads. We encode it in software: a 12-category audit engine that scored 292,000 real records in ~4 seconds, found 45 findings including 12 high-severity gaps, and produced a ranked remediation roadmap. Here's how it works.