Fewer translation layers
The executive question, the SQL, the chart, and the Python that built it live in one project. Nothing is lost between roles.
Introfor executives
What Lanikaia is, and why it isn’t another BI seat.
An overview for executives deciding whether to send Lanikaia to a data leader for evaluation. Three short readings: what changes for the business, what the product actually builds, and why this is not another dashboard, catalog, or notebook layer.
01 / Features
What changes when a question can become a governed layer.
01 / What it is
One place for the question, the code, and the result.
Today, one cross-domain question becomes tickets, SQL, screenshots, and meetings. Lanikaia keeps the question and the pipeline in the same project. The answer is not only a chart; it is a governed Iceberg layer your team can inspect, reuse, and ship.
02 / What it does
A conversation that leaves an operational artifact behind.
A question becomes a chat. The chat becomes Python that runs on Iceberg. The Python becomes a versioned table that downstream systems can rely on. The same conversation produces the chart your board reads, the API another product consumes, and the audit record your security team requires.
03 / What it is not
Not another catalog, not another BI seat.
Lanikaia is not a metadata catalog you decorate with documentation. It is not a BI tool you license per analyst. It is a layer above both, where the question, the analysis, and the artifact live in the same project, governed by the same identity, and stored in the same Iceberg snapshots.
02 / Benefits
What changes for the business when the dashboard becomes a conversation.
Decisions in business hours
Analysts reach the intermediate table they need today, not next sprint. The bottleneck of a centralized data team disappears for routine asks.
Audit by default
Every commit is a versioned Iceberg snapshot. Every grant is logged. The artifact your CFO signed off on can be reproduced one year later.
Engineering kept for engineering
Pipelines that matter are promoted from chat to scheduled workflow. Pipelines that do not get promoted simply disappear. No half-finished dbt models accumulating in main.
03 / vs the BI stack you already own
Three replacements, three ways the unit of work changes.
The category
Tableau / Looker / Power BI
Dashboards as the unit of work. Each chart is an artifact built on top of a curated semantic model. Analysts ship charts; engineers ship the model.
Lanikaia
Lanikaia
Chat as the unit of work. The same chat builds the intermediate table, the chart, and the Python that runs both. The semantic model is composed as you go and versioned per commit.
The category
Atlan / Collibra / Alation
Catalog as the unit of governance. Metadata is documented around tables that already exist. Lineage is reconstructed after the fact.
Lanikaia
Lanikaia
Pipeline composition as the unit of governance. Lineage is the Python you wrote. The catalog is what you produced, not what you remembered to document.
The category
Notebook / spreadsheet sprawl
Each analysis is a Jupyter notebook or an Excel file in someone’s drive. Reproducibility is a personal habit. One year later the cell ordering is gone and the data is stale.
Lanikaia
Lanikaia
Each analysis is a chat with versioned commits and an immutable Iceberg snapshot. One year later the question, the answer, and the data are still aligned.
04 / Next
Two ways to evaluate Lanikaia.
For executives
Schedule an executive briefing.
A 30-minute conversation with the founders. No deck, no demo. We walk through one of your existing data questions and show how Lanikaia would change it.
Request briefingFor the team you’ll bring in
Send your data leader the technical detail.
The architecture, the Iceberg / Polaris integration, the governance model. Built for the Head of Data who has already evaluated the catalog stack.
Open the technical pages →