Customer data platforms are powerful. They’re also notoriously complex.

Now Treasure Data wants to simplify that complexity with code—and AI.

The company announced the general availability of Treasure Code, an AI-native command-line interface designed to transform how teams operate the Treasure Data Intelligent Customer Data Platform (CDP). The pitch is bold but clear: manage your entire CDP as code, automate everything, and let AI handle the heavy lifting.

In a world where CDPs manage hundreds of millions of profiles and trillions of data points, that shift could have significant operational implications.

CDP Complexity Meets DevOps Discipline

Modern CDPs are no longer simple marketing tools. They sit at the center of enterprise data operations, powering segmentation, personalization, customer journeys, and increasingly, AI agents.

But as these platforms scale, manual processes—console clicks, one-off scripts, fragmented workflows—become bottlenecks. Iteration slows. Operational risk increases. Teams grow.

Treasure Code aims to bring DevOps-style rigor to this environment:

• Version-controlled configurations

• Peer-reviewed changes

• Automated deployments

• Instant rollbacks

Instead of operating the CDP through multiple dashboards and manual steps, teams can treat configurations, workflows, and data pipelines as code—fully automated and reproducible.

For organizations already managing infrastructure-as-code, this approach aligns CDP operations with modern engineering practices.

What Treasure Code Actually Does

At its core, Treasure Code is an AI-native CLI that provides programmatic control across:

• Data workflows

• Customer segments

• CDP configurations

• AI agent orchestration

It’s also augmented with Claude Code, enabling natural-language-driven creation and iteration. Users can describe what they want in plain English and generate production-ready SQL, segments, and workflows—subject to human verification.

That human-in-the-loop model matters. In enterprise environments, AI acceleration is only useful if governance remains intact.

Key Capabilities

Natural-Language Execution
Instead of wrestling with complex SQL or CLI syntax, users can issue commands in natural language. The system translates technical intent into executable configurations.

Code-Grade Governance
CDP configurations become version-controlled artifacts. Teams can review changes, manage branches, and roll back instantly if needed.

Unified Command Layer
Treasure Code consolidates fragmented consoles and scripts into a single automation layer, streamlining deployments from development to production.

In short, it attempts to remove friction from CDP operations without sacrificing control.

Rapid Adoption Signals a Pain Point

According to Rafa Flores, Chief Product Officer at Treasure Data, more than a quarter of the company’s customer base adopted Treasure Code within days of release.

That’s notable, especially for a technical product aimed at data engineers and platform teams. CDP users aren’t typically quick to change operational workflows unless the existing system is slowing them down.

And in many enterprises, it is.

Tomohiko Sugiura, Executive Vice President at Dentsu Digital, described the experience as adding “a legion of data engineers” to the team, citing the ability to generate production-ready assets in minutes through plain-language prompts.

For organizations juggling marketing operations, engineering resources, and AI experimentation, that productivity gain could be meaningful.

AI Agents Operating the CDP

One of the more forward-looking aspects of Treasure Code is its positioning as AI-agent-friendly infrastructure.

As enterprises deploy autonomous or semi-autonomous AI agents for campaign optimization, segmentation, or personalization, those agents need secure, governed access to CDP capabilities.

Treasure Code enables AI agents—under supervision—to operate CDP workflows programmatically. That opens the door to:

• AI-managed audience updates

• Automated journey optimizations

• Continuous segmentation refinement

This is where CDPs are heading: from static data repositories to dynamic AI-driven systems. Treasure Code appears designed for that future.

Market Context: The AI-Native CDP Arms Race

The broader CDP market is undergoing a transformation. Vendors are racing to embed generative AI, predictive analytics, and workflow automation into their platforms.

But many AI enhancements sit on top of legacy operational layers. Treasure Code flips that approach by embedding AI into the operational core.

Rather than adding another dashboard with AI suggestions, it redefines how teams interact with the platform itself.

That distinction could matter as enterprises seek:

• Reduced operational overhead

• Faster iteration cycles

• Greater engineering alignment

• Lower risk in production deployments

If Treasure Code succeeds, it positions Treasure Data less as a marketing tool and more as programmable infrastructure for customer intelligence.

The Bigger Picture: Fewer Resources, More Output

Flores emphasized a key enterprise pressure point: doing more with fewer resources.

As customer data grows in scale and complexity, headcount doesn’t always keep pace. Engineering teams are stretched thin. Marketing ops teams are expected to deliver faster personalization cycles.

By automating repetitive technical tasks and introducing AI-assisted iteration, Treasure Code aims to shift human focus toward strategic initiatives rather than operational maintenance.

The result, ideally, is not just efficiency—but agility.

Bottom Line

Treasure Code represents a strategic pivot toward AI-native operations inside the CDP layer. By merging DevOps principles, natural-language interfaces, and AI-assisted automation, Treasure Data is betting that the future of customer data management is programmable, governed, and agent-ready.

If adoption continues at its current pace, Treasure Code could become less of a feature and more of a foundational layer for how enterprises operate their CDPs.

And in a landscape where customer data is both an asset and a liability, tighter control paired with faster iteration is an attractive combination.

Get in touch with our MarTech Experts.