ADEQUATA: Automated data quality remediation

The Problem: Traditional data quality tools stop at monitoring or rely on lightweight query-based patches. For high-value structured and tabular datasets, writing manual SQL rules or relying on generative LLM agents creates immediate engineering bottlenecks, nondeterminism, and hallucination risks. The Solution: Axolotl by ADEQUATA is an end-to-end automated data quality remediation engine. It skips lightweight query-based fixes, using specialized high-fidelity preserving analytical AI/ML to give datasets regenerative, self-healing capabilities with zero rule creation required. Core Capabilities: Automated Deep Remediation: Automatically runs multi-stage feature engineering, anomaly detection, and advanced zero-touch deduplication to fix inconsistencies and inaccuracies within a few clicks. Synthetic Data Imputation: Uses research-grade synthetic imputation to recover missing data gaps with high-fidelity precision, ensuring strict internal consistency. True Stateless Security: Built with a Zero-Footprint Guarantee. All customer data is processed strictly in volatile memory (RAM) with ephemeral container storage and instantly purged upon write-back. No data ever touches persistent storage or trains global models. Zero SQL & Zero LLMs: Bypasses the complexity of rule-based setups and the unreliability of generative AI models for maximum determinism and interpretability.