New IaC-driven governance supports trustworthy customer context

Poor data quality has long been the root cause of mistrust in metrics, turning simple business questions into technical investigations. AI raises the stakes because it turns data quality problems into customer-facing issues that fracture trust and compound quickly.

To provide consistent, positive interactions, AI systems that power customer-facing experiences need on-demand access to fresh, trustworthy customer context.

At RudderStack, we’ve been building the foundation for this since day one: Reliable real-time pipelines for clickstream data, the market’s most innovative customer 360 solution, and governance that enables proactive data quality and compliance throughout the pipeline. Now, we’re adding customers faster than ever, and usage is exploding. In 2025, RudderStack delivered 3.3 trillion events for over 4,000 organizations.

Today, we’re launching new capabilities for the AI era that support assembling and serving fresh, trustworthy customer context:

• IaC-driven governance for strong data quality guarantees,
• Incremental Features for dramatically faster customer 360 modeling, and
• Bot Management and event blocking to give you even more trust your data.

Data quality is non-negotiable for generative AI

As companies race to implement AI, data quality has emerged as the primary blocker to deployment. In the past, when most data use cases involved close human supervision, quality issues could be tolerated and managed reactively. But when automated systems continuously consume data and make non-deterministic decisions in front of customers, data quality is non-negotiable and reactive management fails.

RudderStack already supports proactive data quality management with a data catalog to define schemas and tracking plans to automate validation, but for teams productionizing autonomous AI systems, UI-driven governance isn’t enough. They need stronger guarantees and more efficient workflows. That’s why we’re deepening our governance offering with IaC capabilities that let teams manage their data catalog and tracking plans as code.

RudderCLI for IaC-driven data governance

RudderCLI turns data catalogs and tracking plans into first-class, code-based assets. Instead of managing schemas through manual UI updates, you can define governance rules declaratively, store them in Git, and validate and deploy them through automated workflows. It gives you all the guarantees you’d expect from production software applied to data quality.

RudderCLI delivers:

• Code-based tracking plans and data catalogs: Define events, properties, and schema rules in version-controlled configuration files.
• CI-driven validation: Automatically validate governance changes in CI to catch inconsistencies or breaking changes before they impact production pipelines.
• Efficient, developer-native workflows: Deploy governance changes through code-first workflows that improve reliability without slowing teams down.

This model unlocks benefits that UI-driven governance can’t support at scale. Git-based workflows enable safe collaboration between both humans and AI systems, supporting a world where LLMs can propose changes, and reviews and validation act as guardrails.

The control and guarantees provided by these code-based workflows make it possible for teams to trust, scale, and automate data quality in a world where AI systems continuously consume data and act on it without human supervision.

RudderCLI is currently in alpha. Public beta launches later this month. Learn more in the docs.

Incremental features for faster, more efficient customer profiles

Trustworthy data alone doesn’t make customer context. It has to be assembled into identity-resolved customer 360 profiles that serve as the customer context foundation for every use case. RudderStack Profiles solves for this, and it’s now 5X more performant.

Incremental features optimize how customer profiles are computed in the warehouse by updating only what has changed. This incremental approach significantly reduces compute overhead while preserving correctness, enabling faster profile updates and lower warehouse costs as customer data volumes scale.

Read the Incremental features docs to learn more.

IaC-driven governance enables schema evolution without breaking changes and delivers the foundation for reliable AI workflows. Profiles enables the warehouse to fuel AI, analytics, and activation with fresh customer context.

Together, these capabilities enable data teams to confidently fuel AI, analytics, and activation systems with fresh trustworthy customer context.

Even more control over your event data

But wait, there’s more! To give teams even more control over their event data, RudderStack now includes Bot Management and Event Blocking. Bot Management preserves data quality and ensures downstream consistency by identifying bot-generated activity, including traffic from LLM-powered crawlers and agents, and giving you controls to flag those events for analysis or drop traffic from known bad bots altogether.

Read the Bot Management docs to learn more and join the Beta.

Event Blocking provides broader control, allowing you to drop unwanted events at ingestion, preventing erroneous, deprecated, or temporarily out of scope events from flowing downstream.

Read the Event Blocking docs to learn more and join the Beta.

Get confident in your context

If you’re working on customer-facing AI and want to deliver fresh, trustworthy customer context, data quality can’t be an afterthought. Fix-it-after-it-lands isn’t an option, while managing schemas and validation rules manually leaves too much room for error and won’t scale. You need an efficient workflow with strong guarantees. You need a faster, more reliable way to build customer 360 profiles. And you need total control over your customer data. RudderStack can support your end-to-end customer context workflow.

Get a demo with our team today to see the fresh, trustworthy customer context engine in action.