Cut the cost from human data cleaning by 70%+, skip the headache for preparing/prompting a detailed data remedation plan, minimize the energy consumption and nondeterminism from expert agents coding from scratch for your every dataset, fix the most challenging data quality issues perpetuated in your datasets to restore their full value.
Automates the entire workflow including multi-stage feature engineering, training specialized models, tracking milestones and summarizing results. Resolves the most prominent data quality issues: inconsistencies, inaccuracies, human errors, redundancies, incompleteness, end-to-end automated within a few clicks. No rule creation is required.
No customer data content ever touches our storage or feeds to a global AI model: zero-retention, zero breach risk. Our AI agent operates at workflow level and hands over the heavy lifting to specialized deep pattern-based ML for maximum accuracy & security, and better determinism & interpretability.
Axolotl uses unique research-grade synthetic imputation to recover missing entries with high-fidelity precision. Unlike standard enrichment that drags in errors from external sources of unknown sovereignty, our specialized models guarantee internal consistency and data integrity.
Automatically identifies outliers and unusual patterns in your datasets with ensemble anomaly detection. The solution proactively flags and fixes issues at scale, mitigates risks in your datasets from the point of action.
Integrates with leading cloud data platforms, offers a simple UI with minimal technical knowledge or manual configuration required - our platform solution can work on its own or connect effortlessly to your existing data workflow adding zero complexity.
Our AI data quality product Axolotl is built with research‑grade analytical AI in secure VPC environment strictly adhering data compliances. The cost of a data quality run is measured in Data‑Qualifying Units (DQUs), where the consumed DQUs for each run represents 1GB of logical data successfully remediated, multiplied by the task complexity factor for that run. It features advanced zero-touch deduplication, ensemble anomaly detection, and semi-supervised learning with fully automated built-in pipeline for synthetic data imputation for gap fillings—ideal for high‑value structured datasets with heavy numerical and categorical schema. With minimal no‑code setup and seamless integration, it delivers end‑to‑end data quality solution which actually reliably fixes data quality issues. For each run it also produces a comprehensive data quality report with audits and compliance guidance.
Our engine operates on the logical data volume (the total count of records and attributes), ensuring consistent pricing regardless of your storage format. Whether your data arrives as uncompressed text (CSV) or high-efficiency columnar storage (Parquet), your DQU consumption is calculated based on the logical data remediated.
Most platforms prioritize data ingestion and 'ownership' to create vendor lock-in. We focus on stateless processing, leaving governance & lineage fully under customer control. Our data quality engine utilizes session-based, ephemeral machine learning models that process data in a secure, isolated cloud environment. Our Zero-Footprint Guarantee:
True Statelessness: All customer data is processed as an ephemeral stream. Data is held exclusively in volatile memory (RAM) for the duration of the compute run. No raw customer data ever touches persistent storage disk in our environment.
Instant Termination: Upon successful write-back to your environment, the active session and its associated memory buffers are instantly purged - the data is gone from our end the millisecond the job finishes.
Auditability without Liability: We retain only minimal, anonymized process telemetry (e.g., row counts, processing logic skips, and billing metadata) in a secured archive for dispute resolution and compliance (SOC 2/ISO), as required by law.
Model Integrity: We strictly warrant that customer data is never used to train, fine-tune, or improve our AI agents or any global models.
We provide the fix without the footprint—ensuring your data remains your property, and your security posture remains intact.
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Pricing is driven by remediation volume, not file size. We measure the total number of cells processed to ensure you only pay for the active remediation of your data assets, ensuring consistent performance across high-density datasets.
Every job provides a detailed remediation summary, breaking down the total record-field count processed. This ensures total transparency: while formats like Parquet reduce your storage footprint, our pricing remains strictly aligned with the actual volume of data points remediated, ensuring you pay for results, not file compression.
We adopt powerful vertical scaling which is often more efficient—than horizontal scaling. This approach keeps our platform simpler, faster and securer for your workloads.