Smarter Pipelines with Trusted Metadata

With the launch of Atlan’s App Framework, a new wave of possibilities opens up for the data and AI ecosystem. For the first time, agentic AI systems like Maia, Matillion’s agentic data team, can be directly connected to a trusted source of business context and governance. This connection has the potential to reshape how data teams build pipelines – making them faster, more reliable and more compliant by design.

The Pressure on Data Teams

Data teams today face a constant squeeze. On one side, the business demands faster answers: new dashboards, new metrics, new insights. On the other hand, regulators and executives demand confidence: every number must be consistent, explainable and compliant.

Too often, speed and trust feel like a trade-off.

That’s why the launch of Atlan’s App Framework is exciting. It opens the door for systems like Maia to work hand-in-hand with trusted metadata. Together, they can help data teams deliver faster insights with stronger governance – without forcing a choice between the two.

Maia: The AI Data Engineer

Maia helps data teams build and run pipelines with natural language. Ask Maia to create a job that calculates churn, or refresh a dashboard, and it gets to work instantly.

But like any AI, Maia is only as good as the context it’s given. Without a single source of truth for business definitions and governance rules, even the smartest assistant risks building pipelines that conflict with other teams’ logic – or worse, pipelines that mishandle sensitive data.

Why Atlan Complements Maia

Atlan is where organizations unify their data and AI context, collaborate on and govern data and AI products, and activate context across human and AI experiences. It’s the system of record for context such as:

• Certified metrics (like “monthly churn” or “net revenue”)
• Lineage (where data comes from and where it goes)
• Owners and domains (who’s accountable)
• Governance context (PII flags, sensitivity tags, compliance policies)

If Maia could query Atlan for this context, every AI-generated pipeline would begin on a trusted foundation.

The Potential Business Benefits

1. Faster time to value

No more Slack threads or back-and-forth to find the “right” churn definition. Maia instantly discovers the certified metric in Atlan and generates a ready-to-run Matillion job. What once took days would start in minutes.

2. Trust in every number

Executives ask a simple question: Can I trust this dashboard? With Atlan as the semantic layer, Maia can build only on certified, owner-approved assets. That means dashboards and reports align to the same “source of truth,” eliminating metric disputes.

3. Governance by default

Compliance isn’t an afterthought – it’s embedded. If an upstream table is tagged as PII in Atlan, Maia will automatically suggest masking steps in Matillion to ensure sensitive data is handled correctly.

Closing the Loop: Maia strengthens Atlan with new knowledge/context

The real breakthrough would come when Maia not only consumes Atlan’s context, but also feeds back new knowledge.

Every pipeline built in Matillion generates fresh context:

• Which source tables were used
• What new derived tables or views were created
• Which teams own the job
• When and how often it runs

Instead of leaving that knowledge buried in the transformation pipeline, Maia could automatically write it back into Atlan.

That would keep Atlan a living context store, enriched continuously with real-world usage and provenance from Matillion pipelines. The benefits would include:

• Stronger lineage: Atlan shows not just upstream assets, but downstream jobs and tables created in Matillion.
• Better governance: Compliance teams see exactly where sensitive data flows after transformation.
• Improved collaboration: Analysts browsing Atlan, knowing which Matillion job produced a dataset and who to contact.

Empowering a Data Productivity Flywheel

With Maia and Atlan connected in both directions, organizations could create a virtuous cycle:

1. Start with trusted context (certified metrics, lineage, policies in Atlan).
2. Build faster pipelines in Matillion with Maia.
3. Enrich the catalog automatically as Maia writes back job details, new assets, and lineage.
4. Increase trust and reuse, so the next project starts from an even richer foundation.

This flywheel would mean every new pipeline makes the whole data ecosystem smarter, safer, and faster.

AI + Context = Productivity with Trust

The future of data productivity isn’t just speed. It’s speed with trust and continuous improvement.

By enabling Maia to connect with Atlan through its new App Framework, organizations would move fast and stay governed – while ensuring their metadata stays fresh and valuable.

Maia + Atlan become the AI data engineer who not only builds, but also documents, governs, and improves your data estate over time.