Mage has raised $12M to build AI-ready data and an execution layer that helps teams spend less time on busywork and more time on what matters.

We raised a Series A.

That’s exciting. But the most exciting part is what it gives us the opportunity to build next.

Useful AI at work needs more than a good prompt. It needs trusted context people can inspect, workflows people can recover, and execution that keeps important work moving.

That’s the future we’re building at Mage.

The villain isn’t AI. It’s busywork.

Repeated setup. Stale reports. Context copied into another ticket. Workflows that need someone watching them because one tiny change can break the whole thing.

That’s the work people want help with.

We don’t believe AI should replace the judgment, creativity, or expertise people bring to their jobs. We believe it should support them by handling repetitive work, making complex systems easier to understand, and giving teams more time to focus on the problems that need a human mind.

Good AI doesn’t take meaningful work away from people. It gives more of that work back to them.

Prompts are only as good as the context behind them.

A prompt can sound confident and still be working with incomplete, outdated, or misunderstood information.

Useful AI needs context it can trust:

• Approved data and business definitions
• Lineage and freshness
• Permissions and ownership
• Operational history
• Evidence explaining how an answer was reached

That’s what we mean by AI-ready data.

It isn’t another copy of your data created exclusively for AI. It’s reusable context that helps people and AI understand what the data means, where it came from, whether it’s current, and what should happen next.

Learn more about AI-ready data.

The missing layer is execution.

Answering a question is useful. Turning that answer into reliable action is where the real value begins.

A workflow should be created through conversation, adjusted visually, reviewed in code, recovered when it fails, and governed through one shared operational history.

One workflow. Multiple ways to work with it. No parallel version of the business logic.

That’s why we see Mage becoming the execution layer between data, AI, and the people responsible for keeping important work running.

AI at work has to earn very human trust.

The best AI at work isn’t a mystery box.

It’s a system people can question, change, recover, and trust when production teams depend on it every day.

Teams should always be able to ask:

What changed? Every run should leave evidence, including inputs, outputs, code, owners, and history.

Can we fix it? People should be able to inspect, adjust, recover, and rerun work without losing the story.

Will it stay governed? Permissions, lineage, freshness, and operational state should travel with the work.

AI can suggest the move. Mage is where that move becomes visible, reviewable, and governed.

The next Mage is simple to say.

Add data. Explore data. Use data.

Behind that simple loop is the harder product work of making data useful for AI without hiding the evidence, permissions, or operational state teams need to trust it.

Add data: Bring together sources, pipeline outputs, files, documents, and operational signals as trusted context.

Explore data: Ask questions in plain language and see the evidence, caveats, lineage, and freshness behind the answer.

Use data: Turn trusted context into governed workflows, reports, automations, agents, and actions with clear recovery paths.

$12M in fuel for the execution era.

Mage has raised a $12M Series A led by SineWave Ventures, with participation from Gradient Ventures.

This funding gives us more room to build the system underneath useful AI:

• Conversational workflows people can inspect
• AI-ready context teams can reuse and trust
• Production execution with recovery, lineage, and review

Mage is already used in production by teams operating at significant scale. We aren’t leaving that foundation behind. We’re building on it, helping people work with the systems they already have while making those systems easier to understand, operate, and improve.

Read the full press release.

This chapter starts with gratitude.

Customers have trusted Mage in production, through use cases no roadmap could have predicted.

Builders have opened issues, shared hard-won feedback, challenged the product, and stayed patient while we learned.

To every customer, community member, teammate, partner, supporter, and family member who made room for the work: thank you.

This Series A starts with the foundation you helped build. It gives us more room for what comes next.

Less busywork. More real work.

We’re building Mage for data engineers who want AI to remove toil without flattening the craft.

We’re building it for teams that want important work to become easier, stay governed, and keep happening correctly.

The funding is exciting.

Now comes the fun part.

Try Mage or talk to the Mage team.

Originally published as an interactive story at mage.ai/blog/series-a.