Data Agents for your Business.

The agentic data platform that automates data quality, governance, and observability — end to end. Command your data lake with the precision of a technical blueprint: data agents do the heavy lifting, your team stays in the loop.

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500+ connectors // powered by dlt, DuckDB & Apache Arrow

The architecture

Open components, one pipeline. dlt lands the data, Parquet stores it, SQLMesh models it, and DuckDB serves it to your agents and your team.

Ingest · dlt

Model · SQLMesh

Store · Parquet

Query · DuckDB

The architecture — under the hood

dlt, SQLMesh, DuckDB and Parquet.

datajoi hosts your data on an open stack. dlt (dlthub core) lands it, SQLMesh models it, DuckDB queries it, and it lives as Parquet you can take anywhere.

01 — ingestdlt

dlt (dlthub core)

dlt pipelines pull from 500+ sources, normalise and evolve schemas, and land raw tables with a full load history.

02 — storageParquet

Parquet

Every table is stored as columnar Parquet, an open format any engine can read, so there is no proprietary warehouse in the way.

03 — modelSQLMesh

SQLMesh

Models, tests and virtual environments. Every change is planned, diffed and applied like code, and backfills are tracked.

04 — queryDuckDB

DuckDB

In-process SQL runs directly over the Parquet. Agents and DataStories read the modelled layer with no warehouse between them and your data.

The architecture — command surface

Command & query

Data agents run the command loop end to end — they write, execute, and validate SQL against your lake, and manage the catalog itself. On SQLMesh + DuckDB that means agents register sources, evolve schemas, plan and apply model changes, and backfill what moved: every change a reviewed, versioned plan, every result a DataStory™ your whole team can read.

datajoi workspace: a SQL query for top customers by revenue running against warehouse.orders, a results table (Northwind Ltd $4.21M, Globex Corp $3.08M, Initech $1.94M), live ORDERS → CUST_ORDERS → SUMMARY lineage, and a DataStory noting the top 5 customers drive 61% of revenue.

The architecture — agentic pipeline

Automated at every step.

An agentic pipeline with human-verified, quality-controlled lineage — end to end.

01_connect.sql

Connect your data

Lake schema and lineage read automatically.

02_agent.sql

Agents write SQL

Plain English becomes a query you can check.

03_run.sql

Run & validate

Executes with automated data-quality checks.

04_review.sql

Quality review

Agents flag anomalies; a human confirms edge cases.

05_story.md

DataStory™ delivered

A verified narrative, ready to share.

Verification checks ✓ Schema check passed ✓ Row-count drift within 0.2% ✓ Human review confirmed ✓ Lineage verified 12 sources All checks passing

Pricing

A plan for every data platform

Run your own, run your whole company's, or right-size something custom — datajoi's agents do the heavy lifting.

datajoi platformpro

Pro

$99/mo

For the hands-on data pro

Manage your own data platform
Or manage platforms for your clients
Agents, DataStory™ & verification checks

Start with Pro

datajoi platformMost popular

SMB

$999/mo · up to 10 users

Fits most businesses out of the box

Everything in Pro
500+ existing connectors
Shared workspace for up to 10 users

Start with SMB

datajoi platformenterprise

Enterprise

We'll right-size a plan to your stack

Custom connectors
Data migrations & data modeling
Anything custom, built with you

Talk to us [email protected]

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Reference

Frequently asked questions

What is datajoi?

datajoi is an agentic data platform that automates data quality, governance, and observability end to end. Purpose-built data agents connect sources, write SQL and data models, run quality tests, and document everything — while your team reviews and approves every change before it reaches production.

How do datajoi's data agents work?

datajoi ships purpose-built agents for ingestion, modeling, quality, and cataloging. They work your data stack through MCP, API, and SDK integrations: an agent proposes a change — a new transformation, a schema edit, a quality rule — as a clear, reviewable request in plain language. A human approves, edits, or sends it back. Nothing touches production without a human yes.

What is datajoi built on?

datajoi is built on an open stack. dlt (dlthub core) ingests data from 500+ sources, SQLMesh models and tests it, DuckDB runs the SQL in-process, and every table is stored as open Parquet. Your data stays portable, with no proprietary warehouse or vendor lock-in.

Does datajoi replace my data team?

No. datajoi is built human-in-the-loop by design. Agents handle the repetitive heavy lifting — writing tests, mapping lineage, monitoring pipelines — and your team stays in control with plain-language reviews, one-click approvals, and a full audit trail of who approved what and when. No SQL is required to review changes.

Which data sources does datajoi connect to?

datajoi offers 500+ connectors powered by dlt, with DuckDB and Apache Arrow for compute. It works with warehouses and tools like Snowflake, BigQuery, Databricks, Postgres, dbt, Kafka, and S3 — plus SaaS apps, files, and streams. Data lands from any source in minutes.

What is a DataStory?

A DataStory is datajoi's answer format: ask a question in plain language and get back the generated SQL, the results, and a narrative explanation with live charts — backed by lineage, quality tests, and observability. DataStories refresh on their own, so there is no dashboard to build or maintain.

Early access

Put your data on autopilot.

Join the waitlist and be first to command an agentic, end-to-end data platform — built on an open data stack, with humans in the loop.