One workspace, four disciplines, one perimeter.
Data engineering, data science, private intelligence, and governance — unified on a single visual canvas. The same capability surface as the largest cloud platforms, running entirely inside your environment.
The same power as the giants — with the opposite trust model.
The largest platforms are excellent, and increasingly route your data through hosted AI. 0plus matches their capability surface and adds the Arabic and benchmarking layers they don't lead with — without sending anything to public AI.
| Capability | 0plus | Cloud lakehouse platforms | Low-code DS suites |
|---|---|---|---|
| Visual / low-code pipelines | Yes | Yes | Yes |
| Lakehouse storage (ACID, time-travel) | Yes | Yes | Partial |
| SQL warehouse + semantic layer | Yes | Yes | Limited |
| AutoML + MLOps (deploy, monitor, drift) | Yes | Yes | Yes |
| Governance: lineage, audit, RBAC/ABAC | Yes | Yes | Tiered |
| Private vector / semantic search | Private only | Via hosted AI | Via gateway |
| Market benchmarking (where you stand) | Yes | No | No |
| Arabic-first / RTL / Arabic documents | Yes | No | No |
| No public-AI / LLM egress | Guaranteed | No — hosted LLMs | No — external gateway |
| On-prem / air-gapped by default | Yes | Cloud-first | Mostly cloud |
| Built for non-engineers as primary user | Yes | Engineer-heavy | Yes |
Comparison reflects general positioning of cloud lakehouse platforms and low-code data-science suites as of 2026, not a specific vendor benchmark.
Everything the data journey needs — in one place.
Build pipelines on a canvas, not in glue code.
Connect sources, clean and join data, enforce quality rules, schedule jobs, and watch every run. Batch and streaming, declarative under the hood, with lineage captured automatically.
Train, compare, and explain models — guided every step.
AutoML proposes and ranks models; experiment tracking keeps every run; explainability shows why a prediction was made. Analysts work on the canvas, coders in notebooks — same project, same governance.
The intelligence runs where your data is.
Analysis, modeling, and semantic search over your own documents — including Arabic — all served from inside your perimeter. The engine makes no outbound calls to public AI or LLMs. Verifiable at the network layer.
Every read, transform, and decision is traceable.
Role- and attribute-based access, end-to-end lineage, audit logs, approvals, a data catalog, and data contracts — built into every workflow, not bolted on. Show an auditor exactly where a number came from.
The whole platform. Inside your walls.
We'll stand up a workspace on a sample of your data and walk your team through one pipeline, one model, and one benchmark — in your environment.