01
Conversational composition
Each chat owns one layer and assembles the result by combining ops (fetch / add / filter / aggregate / visualize). The semantic model and business ontology evolve with the layer, not in a separate documentation tool.
01 / Product
A workspace that turns questions into governed Iceberg layers.
Lanikaia is an agentic data platform for modern data teams. Domain experts, analysts, engineers, and scientists share the same project, build the same pipeline, and read the same semantic model in the language each role prefers.
02 / How it works
The unit of work is a chat that builds a layer.
Open a chat against a layer. Ask in plain language. The agent maps the question to your business ontology, generates code and dbt semantic assets, runs them against Polaris-managed Iceberg, writes the result, and explains what it did.
Each chat keeps a working draft and a list of commits. Branch a commit to try a different aggregation. Promote a commit to a workflow when it is the version you want to ship.
- 01ingestLocal files, HTTP, dbt SL, SAP, BO
- 02foundationCleaned, typed, schema-stable
- 03enrichedJoined, derived, dimensional
- 04aggregateGroup-bys, daily / monthly rollups
- 05cumCumulative measures across time
- 06joinCross-domain composition
- 07rollingWindowed and lagged features
- 08metricsdbt Semantic Layer / ontology-backed metrics
- 09analyzeStatistical and exploratory analysis
- 10testingHypothesis tests, validation
- 11predictML / classification, regression, time series
- 12optimizeSearch, scenario, what-if
- 13visualizePlotly + ECharts charts and dashboards
- 14workflowPromotion, schedule, monitor
03 / Who uses it
Four lenses on the same project.
Domain expert
Asks the question in their own language. Reads the explanation, the chart, and the underlying number side by side. Decides without learning SQL.
Analyst / BI
Composes intermediate tables in chat, branches alternatives, and pins what works. Each commit is a reproducible artifact, not a one-off.
Data engineer / SRE
Promotes a chat to a workflow. Inspects layers, lineage, and code. Diagnoses a broken pipeline by layer in five minutes, not five hours.
Researcher / scientist
Iterates models inside the same chat that owns the input table. Uses Polars, scikit-learn, LightGBM, statsmodels, UMAP. Versions every run.
04 / What it does
Six capabilities, no decoration.
02
Real code and catalog state under the chat
The agent generates code and dbt semantic assets that run against Polaris-managed Iceberg tables. Output is auditable code, catalog state, and a versioned table, not a screenshot.
03
One project, four lenses
Domain experts read prose. Analysts read tables. Engineers read code and lineage. Scientists read notebooks. Same project, four lenses on the same artifacts.
04
Publish as API or MCP
Promote a dataset or function to a versioned HTTP API or an MCP server. Other agents and applications consume the result without going through the UI.
05
Governed by default
Polaris catalog scopes every project. Identity and permissions flow from your IdP. Reads, writes, and grants are audit-logged.
06
Lossless intermediate
Every intermediate table is an Iceberg snapshot, not a CSV. Schema changes do not silently break downstream. Time-travel and lineage are first-class.
05 / Next