Data and AI execution platform
We raised a Series A. Now comes the fun part.
The fun part is making useful AI work for real teams: trusted context people can inspect, workflows people can recover, and execution that keeps important work moving.
Ask
What comes next?
Mage
Less busywork. More real work.
We raised $12M to build the execution layer for AI that helps people create, inspect, recover, and govern real work.
Scene 01 / Busywork
The villain isn't AI. It's busywork.
Repeated setup. Stale reports. Context copied into another ticket. A workflow that needs someone staring at it because one tiny change can break the whole thing.
Aim here
Repetitive work
Good AI doesn't take the work away from people. It gives the work back to them.
Scene 02 / Context
Prompts are only as good as the context behind them.
Useful AI needs approved data, business meaning, lineage, freshness, permissions, and a way to explain which evidence shaped the answer.
Proof
Approved context
AI-ready data isn't a buzzword here. It's reusable context people can inspect.
Scene 03 / Execution
The missing layer is execution.
A workflow should be created through conversation, adjusted visually, reviewed in code, recovered when it fails, and governed from one shared operational history.
Live
One workflow
One workflow. Three interfaces. No parallel reality for the business logic.
Trust test
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.
01
What changed?
Every run should leave evidence: inputs, outputs, code, owners, and history.
02
Can we fix it?
People should be able to inspect, adjust, recover, and rerun without losing the story.
03
Will it stay governed?
Permissions, lineage, freshness, and operational state should travel with the work.
What we're building
The next Mage is simple to say.
Add data. Explore data. Use data. Behind that simple loop is the harder product work: making data useful for AI without hiding the proof, permissions, or operational state teams need to trust it.
01
Add data
Bring in sources, pipeline outputs, files, documents, and operational signals as trusted context.
02
Explore data
Ask questions in plain language and see the evidence, caveats, lineage, and freshness behind the answer.
03
Use data
Turn context into governed workflows, reports, automations, agents, and actions with recovery paths.
AI can suggest the move. Mage is where the move becomes visible, reviewable, governed work.
The raise
$12M in fuel for the execution era.
Mage has raised a $12M Series A led by SineWave Ventures with participation from Gradient Ventures. The round gives us more room to build the system underneath useful AI: workflows people can create, inspect, recover, govern, and trust in production.
Conversational workflows people can inspect
AI-ready context teams can reuse and trust
Production execution with recovery, lineage, and review
Looking for the official details? Read the press release.
The people behind the platform
This chapter starts with gratitude.
Customers have trusted Mage in production, at scale, through use cases no roadmap could have predicted. Builders have opened issues, shared hard-won feedback, pushed 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 and gives us more room for what comes next.