How Evidence-Based AI Engineering works in practice.
AI ENRICHMENT PIPELINE
A sales intelligence company's AI enrichment system tried to handle sixteen different reasoning patterns with a single prompt. We decomposed it into testable steps and took it from 40% to 95% — same models, same data.
Read case studyEDGE AI FOR FINANCIAL DOCUMENTS
A fintech company needed frontier-model capabilities on-device, where only edge models could run. We reframed the problem from reasoning to classification and built a three-layer architecture that doesn't need the model to think.
Read case studySURVEY ANALYTICS AGENT
A financial research firm needed reliable AI-driven survey analysis. Every LLM approach failed — until we stopped treating spreadsheets as text and started treating them as databases. Accuracy went from ~50% to 95%.
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