09/18/2026
๐ ๐๐-๐๐๐๐ค๐๐ ๐ก๐๐๐ฅ๐ญ๐ก๐๐๐ซ๐ ๐๐๐ ๐ฉ๐ซ๐จ๐ฏ๐ข๐๐๐ซ ๐ฐ๐๐ฌ ๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐ง๐ ๐ฆ๐ข๐ฅ๐ฅ๐ข๐จ๐ง๐ฌ ๐จ๐ ๐๐จ๐ฅ๐ฅ๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐๐๐๐จ๐ฎ๐ง๐ญ๐ฌ ๐๐๐ข๐ฅ๐ฒ ๐ญ๐ก๐ซ๐จ๐ฎ๐ ๐ก ๐ ๐ฅ๐๐ ๐๐๐ฒ ๐๐ซ๐จ๐ฉ๐๐ง๐ฌ๐ข๐ญ๐ฒ-๐ญ๐จ-๐๐๐ฒ ๐๐ง๐ ๐ข๐ง๐.
The technology worked. But the business couldn't clearly see why predictions were being made, which rules were driving outcomes, or whether those rules were still valid.
45+ business rules shaped daily collections. Only 12 were documented. The engine was at end of life, with no explainability behind its decisions.
Replacing the engine wasn't enough. The business needed to understand, validate, and trust the logic first.
Veltris decoded the legacy engine through rule mining and human validation, rebuilt the logic into a validated rulebook, and delivered an AI foundation with 100% SHAP-based reason codes for every prediction.
ML models were gated by 95%+ rule parity and 0.8+ AUC validation thresholds.
A $97K six-week pilot unlocked a 14-project AI mandate.
From black box to explainable AI.
Read the full case study: https://na2.hubs.ly/H07Vjtq0