Auditar provides an AI operational control layer that captures raw inference data in real time and instantly reconstructs the exact decision logic of any model into a regulator‑ready, human‑readable audit without accessing proprietary model weights. By turning outcome monitoring into decision governance, it reduces investigation time from days of manual log hunting to milliseconds, helping organizations pinpoint root causes of bias, fraud or compliance issues in autonomous systems.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations using AI-driven decision systems often detect issues only through outcome alerts—such as spikes in loan rejections or bias warnings—without insight into the underlying model logic. Investigating these anomalies requires manual log analysis across multiple teams, leading to delays of days and potential regulatory non‑compliance.
Solution
Auditar provides an AI operational control layer that ingests raw inference data in real time and reconstructs a complete, human‑readable audit trail of each model’s decision pathway within milliseconds. By operating in a zero‑knowledge, sovereign‑ready architecture, it delivers regulator‑ready traceability without accessing proprietary model weights or disrupting critical production pipelines. The platform isolates feature contributions and logical nodes, enabling instant root‑cause diagnosis for any unexpected outcome. This capability transforms outcome monitoring into proactive decision governance, reducing investigation time from days to sub‑second latency.
Target Audience
Primary customers are financial institutions, fintech platforms, and regulated enterprises that deploy AI models for credit scoring, fraud detection, or risk assessment and require auditable decision transparency.
Features
- Real‑time ingestion of raw inference data to capture every decision event
- Millisecond‑scale reconstruction of the exact logic pathway for any AI model
- Zero‑knowledge observation layer that preserves model confidentiality while exposing feature weights and decision nodes
- Human‑readable, regulator‑ready audit reports generated without touching model parameters
- Non‑intrusive integration that adds 0% disruption to existing critical system paths
- Dashboard view linking decisions to specific feature contributions (e.g., regional risk thresholds, debt‑to‑income ratios)