iiDrak is five products on one control plane — lakehouse compute, low-code AI apps, model operations, a federated catalog and zero-trust governance. Structured or unstructured, streaming or batch, cloud or air-gapped: it runs where your data already lives.
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The blockers are rarely the models. They are the estate underneath: fragmented sources, unusable unstructured data, no path from notebook to production, and governance that arrives after the fact.
Tables in six systems, plus the contracts, scans, tickets, images and audio that hold the real context. Most platforms only serve the tables.
Notebooks and demos are easy. Registry, deployment, guardrails, evaluation and an audit trail are where projects die.
Sovereignty, latency, classification and cost keep critical workloads on-premises. A platform that only runs in one vendor’s cloud cannot serve them.
Query engine here, catalog there, MLOps from a third vendor, annotation from a fourth. Five contracts, five consoles, five security reviews.
Table permissions were hard enough. Now vector stores, embeddings and model outputs need the same controls — and most policy engines have never seen them.
One platform that reads any source, handles any data type, and carries a model from experiment to governed production — in your cloud, your data centre or an air-gapped room. Five products. One control plane. One security review.
Compute, application building, model operations, metadata and governance — built as one system rather than bought as five. Each product is independently deployable, and they share one control plane, one identity model and one policy engine.
The open lakehouse for AI, anywhere. Structured tables, documents, images and telemetry on any storage layer — with MPP querying across sources in place, streaming and batch pipelines, SAGE natural-language analysis and full time-travel versioning.
Build production AI applications without a PhD in MLOps. Low-code drag-and-drop workflows, LLM agents with tools and memory, built-in VectorDB for RAG over your unstructured data, and an Annotation Studio for the humans in the loop.
Import models, serve them behind traffic-split endpoints, wrap them in guardrails, and prove EU AI Act compliance — alongside scheduling, experiment tracking and the model registry.
Every connected system indexed in place — Databricks, Oracle, Postgres, Unity Catalog, SharePoint, Sybase, object storage and the lakehouse — with lineage, a 474-term ontology graph, data quality testing, a data marketplace and an AI Context Center.
Zero-trust governance that covers AI, not just tables. Fine-grained RBAC and masking, plain-English policy previews, controls over vector stores and embeddings, and PII / GDPR / HIPAA / SOX frameworks out of the box.
150+ pre-built connectors across warehouses, lakes, databases, SaaS, object stores, message buses and file shares — plus the unstructured content most platforms leave behind.
"We evaluated Databricks and Snowflake extensively. Neither could give us a credible on-prem story for our classified environment. Nexaris was the only platform that didn't ask us to rethink our security posture to fit theirs."
"Our Databricks bill was growing 40% year-over-year and none of our business outcomes were scaling proportionally. Nexaris gave us a predictable cost model and we didn't lose a single feature we depended on."
"The Wa-Sul AI Studio let our data engineers build and ship production RAG pipelines in weeks, not quarters. Our data science team used to be the bottleneck. Now they're the accelerator."