How Evidence-Based AI Engineering works in practice.
FROM UNRELIABLE PIPELINE TO PRODUCTION DISCIPLINE
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 studyWHEN THE MODEL CAN'T REASON
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 studyTHE SPREADSHEET ISN'T TEXT. IT'S A DATABASE.
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%.
Read case studyAI CAN WRITE SQL, BUT CAN'T REPLACE YOUR ANALYST
A SaaS company built a natural-language BI system over HubSpot and Sage. The LLM could write SQL but not like an analyst — until we gave it business context through dynamic few-shot examples. Accuracy went from 75% to 97%.
Read case studyTESTIMONIALS ARE EVIDENCE. NOT MARKETING COPY.
A B2B company built a chatbot where prospective customers ask about SaaS products and get answers grounded in real customer testimonials — quoted, attributed, and refused when evidence doesn't exist.
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