Type a data task in plain English. Mage AI plans it, writes the code, and shows you the checks before anything runs.
Works with the stack your data team already runs
Sample connector set — illustrative names.
Keep Stripe customers synced to Salesforce every hour.
I created a workflow with sources, checks, and a review step.
Unify. Retire scattered scripts, one-off jobs, and manual handoffs. One workflow layer carries data, and its context, from source to action.
Build. Mage AI turns a plain-English prompt into a production AI workflow, with reusable business logic you can still edit by hand.
43 records repaired and written
CompleteRepair. Failed workflow runs are caught, replayed, and recovered automatically — before anyone downstream notices the data is late.
Standardize. Messy fields and legacy logic become clean, validated data models you can reuse across every workflow.
Deliver. Push trusted data into dashboards, products, APIs, automations, and the agents that need real, current context.
Upstream data / Current
Write access / Confirmed
Resume from / Transform
Govern. Keep operational risk in check with visibility, permissions, approvals, and an audit trail on every workflow run.
Every AI workflow stays editable, inspectable, testable, versioned, and observable. Mage AI speeds up the work without hiding how your data systems actually run.
Everything managed.
Learn more
Runs in your infrastructure.
See a demo
Keep customer dashboards current and trustworthy.
Try itPrepare validated context for our product agent.
Try itKeep Stripe and Salesforce in sync every hour.
Try itRank the accounts that need attention each week.
Try itRepair failed workflows before downstream teams notice.
Try itTurn incoming vendor files into a governed dataset.
Try itDescribe the outcome in a sentence — Mage AI reads the intent, plans the extract, transform, validate, and load steps, and generates a runnable AI workflow you can inspect before anything executes. Every step keeps the context of where the data came from.
All three, depending on the mode you pick. Mage AI can output a declarative pipeline with blocks and a schedule, a SQL transformation, or a Python script with the same logic, so you can use the result in the workflow your team already runs with the same data context attached.
Mage AI connects to 40+ sources and destinations out of the box — including Stripe, Salesforce, warehouses like Snowflake and BigQuery, object storage, and REST APIs — and new connectors can be added without rewriting existing workflows or losing their context.
Every AI workflow ships with generated checks — row counts, null checks, deduplication, and schema drift — that run before data is written downstream. The platform also tracks every execution, so each workflow run carries the context of what ran, what changed, and what failed.
Either. The managed cloud handles infrastructure, scaling, and upgrades for you; the self-hosted build runs your workflows entirely inside your own environment when data residency or network isolation matters more than convenience.
Failed AI workflow runs are caught automatically, retried where it is safe to do so, and repaired from the last good checkpoint, so a broken upstream file or a flaky API rarely means a manual rerun or a late dashboard.
Mage AI is an independent workflow interface. Product names and brands mentioned on this site belong to their respective owners and do not imply endorsement.
