Six situations, each with its limits stated.

Audit & compliance

Regulated reporting with verifiable evidence

Produce regulatory and board figures from pipelines that leave a signed, offline-verifiable record of every run — so the evidence behind a number survives longer than a dashboard screenshot.

Keep data in-country, in-network

Data residency and sovereignty

Run pipelines, a lakehouse and analytics entirely inside your own network — with no hosted control plane, no telemetry and no outbound dependency — so residency obligations are met by architecture rather than by contract.

Move off per-row pricing

Replace hosted ELT with self-hosted pipelines

Move pipelines off a hosted ELT service onto an orchestrator you run yourself — with a visual canvas, SQL and Python steps, resumable runs and per-environment licensing that does not grow with your row count.

Medallion, modelled, enforced

A governed lakehouse you operate

Build a bronze–silver–gold lakehouse on open table formats and your own object storage, with contracts enforced between layers, dimensional models as first-class objects, and a SQL endpoint that applies row-level security.

Dashboards without the export

On-premises analytics with governed answers

Dashboards, paginated reports and a governed query layer that run inside your network against your own warehouse — where every number on a screen can say which metric and which query produced it.

Your model, your keys, your network

AI on your data, on your terms

Use AI to draft pipelines, explain failures and answer questions about your own data — with a model you choose, including one running fully offline, and with every tool scoped to the permissions of the person asking.