AI/ML-native automations that turn months of work into minutes
Proprietary AI/ML workflows, trained on hundreds of enterprise deployments, that keep the cross-estate layer self-maintaining. Deploy in clicks. Upgrade without breaking anything. Govern at scale without a standing army.
Every data task ships pre-governed.
Ingest a file, mirror a system, publish an API — governance, quality, identity, and scheduling are part of the operation, not seven separate workstreams. Best practices baked in, not bolted on.
CDC mirroring without Kafka — replicate source systems directly into Iceberg, near-real-time, one click. Mirrored data immediately inherits identity, catalog, lineage, and quality.
Single-operation ingestion — auto-detects format (CSV, Parquet, EBCDIC, Excel), suggests transformations, applies encryption and type conversion inline. Turn it into a scheduled job, API endpoint, or data product in the same workflow.
Mutation Hooks — every Airflow task, Spark job, and JupyterHub session automatically inherits user identity, permissions, resource queue, and namespace context. No manual configuration or security gaps.
Want to build your own? Full API, SDK, and template access.
Your engineers stop maintaining plumbing and start delivering results
Spend 80% of time building, not just maintaining
Your best data engineers currently spend >80% of their time on maintenance — patching, upgrading, firefighting, manual wiring. NexusOne inverts this: 80% of engineering time goes to AI applications, data products, and the work the CEO keeps asking about.
Self-sufficient operations.
The automations don't just accelerate deployment — they keep the estate running with auto-healing, continuous monitoring, self-generating governance.
