Resources
Blogs, case studies, whitepapers and field guides from the DMOOP team — for marketers shipping campaigns, not benchmark posts.
We pulled 1,402 marketing articles from across the public web over 8 weeks. The asset-type distribution shocked us. Three observations that should change how marketing content gets produced.
From 229 to 854 training pairs in a week using WizardLM-style evolution. The architectural decision, the bug that nearly killed it, and what we learned about LLM data augmentation.
What 'enterprise-pitchable' AI safety actually looks like in production. Input moderation, output moderation, injection detection, PII redaction — and the order they should run in.
AI Overviews now answer ~47% of B2B queries without a click. Here's the exact six-move playbook to be the source the model quotes — schema, structure, signal density.
Why we organized DMOOP's training corpus as a cross-product of 13 asset types and 13 marketing intents — and what we'd do differently if we were starting over.
Six months of running production marketing AI on Groq + Llama vs the alternatives. The cost math, the latency math, the quality gap that closes monthly, and the fallback chain that keeps us shipped.