The platform

Your whole data estate, one conversation

Kaarvi replaces the tool-hopping — profiler here, catalog there, BI somewhere else — with thirty conversational skills that plan, execute and verify the work while you watch.

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Query in plain language

Ask questions of any dataset and get answers with charts — natural language in, verified SQL underneath.

›Which districts had the highest failure rate last quarter?
›Plot monthly volume by region for 2025.

Data quality & profiling

Statistical profiling, anomaly detection over every row (no sampling), key discovery, validation, and AI-proposed fixes with a preview-then-confirm gate.

›Profile this dataset and tell me what looks wrong.
›Find anomalies in sensor_readings and propose fixes.

Governance & compliance

PII detection, classification, policy evaluation and enforcement, compliance scanning, and a governance status view — with your corrections feeding back into calibration.

›Scan the customer tables for PII.
›That column isn't PII — recalibrate.
›Apply the retention policy — dry-run first.

Ingestion & connections

Connect live databases, browse schemas, pull tables, or drop files straight into the conversation. Search a governed Smart Catalog of everything you own.

›Connect to our Postgres and pull the orders table.
›Ingest this CSV and add it to the catalog.

Transform & automate

Run pipelines, get template suggestions, schedule recurring questions, and trigger work automatically when data changes — recurring costs shown before you commit.

›Run the nightly dedup pipeline.
›Every Monday 8am, send me last week’s quality summary.

Dashboards & publishing

Turn conversations into governed dashboards, publish verified reports where every number carries a receipt, and expose pipelines as live API endpoints.

›Build a dashboard from this analysis.
›Publish this as an API my app can call.

Lineage & impact

Trace where data comes from, review lineage across the estate, and run column-level downstream impact analysis before you change anything.

›Where does revenue_usd come from?
›What breaks if I drop customer_id from staging?

Forecasting & diagnostics

Trend forecasts and dataset health computed over all rows via SQL pushdown, plus agentic root-cause analysis when something moves and you want to know why.

›Forecast throughput for the next 90 days.
›Why did rejects spike in March?

Synthetic data

Generate privacy-safe synthetic datasets in three modes — from scratch, from a schema, or shaped like an existing dataset — previewed before saving to the catalog.

›Generate 10k synthetic customer records shaped like this table.

Under the hood

Agentic, and accountable for it

Observable by design

Agent turns stream stage-by-stage over SSE. The steps you watch in the product are the actual execution log, not an animation.

Preview → confirm for anything that mutates

Every skill that changes data produces a concrete preview — the exact rows, violations or costs — and holds for your confirmation.

Tenant isolation on every path

A central organization-scope gate covers every skill in every lane, with RBAC, column masking and audit logging beneath it.

Pushdown, not sampling

Profiling, anomaly detection and forecasting compute in the engine over all rows. Designed for millions of rows and hundreds of columns per dataset.

Resilient model layer

A provider fallback chain with circuit breakers sits behind every AI step, and model output is validated before anything acts on it.

Hardened code sandbox

Generated analysis code executes in a locked-down subprocess sandbox with resource limits — never in your infrastructure directly.

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