Trust Infrastructure for AI Commerce
RankingMaster, Kaizen Loops, and 26-signal scoring — built 100% on Snowflake. The trust layer that makes ChatGPT cite your products.
AI decides who gets SEEN and who gets to ACT — SuilaAI is the score AI checks for both. Product Trust is the SEEN half of the same 300–850 bureau that clears agents to transact. One score, two verbs →
Explore the Trust Stack
Six components. One unified pipeline. Click a tab to dive deeper.
ML-Powered Trust Weight Learning
RankingMaster is a Ridge Regression model that learns ChatGPT's actual citation behavior. It discovers the optimal weights across four trust pillars — achieving R² = 0.983 accuracy. Like FICO® for content trust, scoring 300–850.
Open RankingMasterTwo interlocking loops drive the system: the Learning Loop (KLL) probes ChatGPT to learn what trust signals matter, while the Execution Loop (KLE) uses Cortex AI to improve content locally — minimizing expensive API calls while maximizing trust scores.
The entire trust pipeline runs inside Snowflake using Cortex AI, Python 3.12 UDFs, and stored procedures. DDL for tables, views, tasks, and Snowsight dashboards — all included. No data ever leaves the customer's Snowflake account.
Each signal is a standalone Python module implementing a deterministic scoring function. From Provenance (C2PA verification) to Temporal Stability, Semantic Quality, and Lineage Attribution — the full spectrum of trust computation.
One bureau. One 300–850 score. Two verbs.
Product Trust is one entrance to the same bureau
Make AI Cite Your Products
Try the live trust pipeline demo. See your product scored across 26 signals. Improve with Cortex AI recommendations.
Snowflake-native. No data leaves your account.