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Scalable Advanced Data Architecture

Analytics, governance and AI all run on one platform inside your environment, supported by the team that built it.

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The governance dashboard, showing data quality scores and compliance status across the estate.
The visual pipeline builder, showing a source connected through to a governed dataset.
Visual pipeline building, from the live platform.

Five dashboards for a government agency.

The dashboards cover collection, finance, investment and placement, on the client's own infrastructure.

On their infrastructure

The platform sits in the client's own environment, under their change control.

Open source throughout

Every component is open source, so there is no annual licence to renew.

Three months of support

Runs from handover, around the clock, with a four hour response.

Governance ships with the platform.

A catalog, lineage, quality checks, classification and access control are part of the install.

Governed inside

Access is granted by role, personal data is masked, and every figure traces to its column.

Open formats

The platform writes Iceberg and Parquet tables and speaks SQL, so your BI tools keep working.

Built to grow

It is designed for very large data volumes, and to add capacity as the estate grows.

Data for AI

AI projects stall on data nobody trusts. This keeps it described and governed.

Deployed inside your environment.

Access control configuration mapping roles to the datasets they may read.

On your infrastructure

It runs on premise, in a private cloud you hold, or a mix of both.

Governed by design

An audit becomes a lookup, because every figure is already traceable to its column.

Supported from Jakarta

The engineers who built the platform are the ones who answer the ticket.

What we put on screen.

We show five parts of the working platform in a live session.

Connectors configured against several source systems.

Connectors

Reads from the systems you already run.

Column level lineage tracing a field back to its source.

Lineage

Follow any figure back to the column it came from.

Data quality checks running against a dataset, with the result per rule.

Data quality

Checks run on a schedule and report what passes.

Scheduled jobs with their run history and retry state.

Scheduling

Jobs run on a schedule, retry on failure, and raise alerts.

An aggregate query running against the analytical engine.

Query engine

Aggregations run over large volumes, inside your own environment.

90%TKDN · SELF DECLARED

Built and supported in Indonesia.

Our engineers in Jakarta build the platform. The 90% figure comes from that work.

The figure is self declared. Certification is in progress through an appointed verification body, and the certificate it issues is what a procurement process will ask to see.

Bring us one domain.

We run a short session to see whether the platform fits your data.

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