Legacy tools rely on manually defined business rules that are time-consuming to set up and expensive to maintain, often requiring an entire data team, prolonging Time-to-Value (TTV). Whilst ADEQUATA's Axolotl platform is an infrastructure-level data quality tool, it believes most business rules are already embedded in and self-explanatory by the statistical distributions of the datasets themselves and uses ML-driven data remediation to identify the inherent latent logic patterns and distributions within your dataset to fix issues automatically, without requiring you to write a single rule, promoting self-healing intelligence.

No. LLMs and agents are best suited for workflow automation, not core data integrity. It also exposes great security risks because there is no way your LLM-based agents can assure you that they will not overlook or misinterpret your DQ objectives or execute your remediation implementation risk-free and error-free despite your guardrails. This is even presuming that you can specify a detailed remediation plan without allowing your LLM to read your high-value data at the content-level (i.e., row records) first. Axolotl uses analytical, research-grade ML models for high-precision remediation, only employing LLMs or agents where it is complete safe so your valuable data records are out of their reach, ensuring your structured data is fixed with deterministic executions and mathematical accuracy to meet enterprise-grade precision, interpretability, productivity, security and compliance standard requirements.

It provides an end-to-end orchestrated and automated data remediation pipeline for resolving most common yet critical data quality issues such as inconsistencies, inaccuracies, human errors, redundancies, and incompleteness exhibiting an self-healing capability for your structured datasets.

Unlike standard enrichment that pulls in potentially messy and unverified external data which puts a big question mark on data sovereignty and ownership, our models use high-fidelity synthetic imputation to recover/self-heal missing entries based on the internal consistency and integrity of your own dataset.

Axolotl is a specialist engine for high-value structured datasets (tabular data in csv and parquet files). We focus on complex schema with heavy numerical and categorical contents where precision is non-negotiable.

We operate under a Zero-Persistence Framework. Your data is processed as an ephemeral stream in volatile memory (RAM) and never touches a persistent storage in our environment. We provide the "fix" without the footprint.

Strictly No. We warrant that customer data is never used to train, fine-tune, or improve our AI agents or any third-party global models.

Once a remediation job is written back to your environment, the active session and its memory buffers are instantly purged. We retain only minimal, anonymized telemetry (pipeline metadata) for compliances and billing.

Yes. As far as automations can go, we still believe human guardrails are non-negotiable and compliance-essential. Our architecture enforces a Human-in-the-Loop (HITL) Quality Gate. You receive a "Proposed" version and a comprehensive audit report. No data is promoted to your environment without your explicit "Accept" the certification. If you reject a fix, the system will simply persist your original/last-accepted copy and mark the run as complete.

Yes. We can provide our Security Whitepaper and a standard Data Processing Agreement (DPA) upon request to simplify your compliance and legal review.

We offer effortless integration with major leading cloud platforms including Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, Snowflake and Databricks. Simply verify your identity securely then define your dataset read and write paths - flexibly portable to your existing infrastructure data pipelines with zero added complexity.

Most DQ platforms try to "own" your data to create vendor lock-in. Our stateless approach keeps governance and lineage fully under your control - the content of your datasets never shows up in our backend storage system. We spin up dedicated, session-specific ML models that dissolve the moment the task is complete. You have full flexibility of controlling what datasets or which data path to run and write back.

No. Our engine utilizes a powerful vertical scaling approach in a secure VPC environment. This makes it faster and more secure than traditional horizontal scaling.

We use Data-Qualifying Units (DQUs). 1 DQU represents 1GB of logical data successfully remediated, times by a configurable task complexity factor.

No. Pricing is driven by logical remediation volume (the count of records and attributes), not storage format. Whether your data is a raw CSV or a compressed Parquet file, you only pay for the actual volume of data points remediated.

Transparency is our priority. The fixed tier pricing and on-demand overage when you top up for extra DQUs are all you pay, no extra hidden fees. Every job provides a detailed Data Quality Report entailing the amount of DQUs consumed so you can audit exactly what you are paying for.

Some datasets require more intensive ML processing (e.g., complex multi-stage feature engineering or precision tuning for deep synthetic imputation). The complexity factor ensures you pay a fair price based on the actual compute effort required to heal your specific dataset.