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ADEQUATA - Axolotl

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ADEQUATA Axolotl is an automated data quality remediation engine built for structured and tabular datasets. Powered by orchestrated deterministic ML pipelines, Axolotl automatically detects, resolves, and self-heals data anomalies without requiring a single manual rule or SQL statement, cutting data cleaning costs by over 70%. Unlike black-box tools wrapped on LLMs, specialized localized models eliminate hallucinations, misinterpretations, and non-determinism while maintaining human-in-the-loop approval guardrails. Core features include automated schema standardization, fuzzy record deduplication, ensemble anomaly detection, and synthetic data imputation. Security by design: Axolotl operates on a stateless zero-persistence architecture. Datasets are remediated entirely in memory on short-lived compute nodes with swap and core dumps disabled. Dataset contents are never written to disk in ADEQUATA’s environment and are never retained post-execution.

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