6 articles
The ingest-classify-extract pipeline is not the hard part of document AI. What breaks in production is messy scans, tables, and knowing when to trust an extraction — here's the review-queue pattern that actually works.
Most SaaS products need one or two AI features done well, not nine done shallowly. A filter for telling which pitched features are load-bearing and which are decoration nobody uses.
The 838%-ROI claims in vendor pitches are built on assumptions that don't survive scrutiny. A grounded formula for AI automation ROI, and the costs that get left out.
A decision framework for RAG vs fine-tuning — what each actually fixes, when the combination is right, and the volume threshold where fine-tuning starts paying for itself.
A build vs buy framework for AI features — where buying wins, where a thin wrapper is genuinely correct, and the three conditions that justify building AI infrastructure yourself.
Where the money goes when you build an AI product MVP — engineering time, model inference, data preparation, evaluation — and which scope decisions move the total most.