ARA

ARA provides a robust infrastructure layer designed to record and manage every decision made by predictive models. Key features include: * Replay past decisions with exact context * Persistent entity intelligence * Immutable, cryptographically chained records * Synchronous operation in the serving path This system addresses the critical gap where models make decisions but immediately discard the precise inputs, model version, and surrounding context. By binding entity data, timestamp, features, and the decision into a permanent, structured record at the moment of inference, ARA creates a comprehensive memory for your predictive stack. This enables detailed incident debugging by transforming it into a simple query, highlights data drift per entity, and facilitates training data joins without label leakage. ARA integrates seamlessly alongside existing infrastructure, capturing decision records automatically. It can also function as a feature store, ingesting training data once and serving features at low latency. The platform ensures that the feature-decision mapping is never lost at inference, allowing for a closed-loop system where entity evolution and system behavior are continuously monitored. Ideal for engineering and data science teams requiring high accountability and auditability for their predictive systems, especially in regulated industries like credit, fraud detection, KYC, and insurance. The Community Edition runs entirely on your infrastructure and can be set up quickly, with SDKs available on PyPI and Maven Central.