Treasure AI launches agentic platform for marketers

Treasure AI has introduced an agentic experience platform designed to automate customer engagement and marketing execution across channels, using AI-driven workflows and unified customer data.

The platform aims to shift marketing operations from manual processes to continuous, AI-assisted execution, where systems analyse customer signals and trigger actions without requiring constant human input.

The company has repositioned its offering around an AI-native architecture that integrates customer data, automation tools and decision-making systems into a single environment.

Treasure AI coordinates human users, AI agents and activation tools to deliver customer experiences at scale. The platform continuously analyses behavioural data and determines the most relevant message, channel and timing for engagement.

"For over a decade, we've been the trusted data foundation for the world's top brands. That doesn't change, but our charter has expanded to help them better compete in the age of AI - from providing software that humans operate to delivering outcomes that governed AI agents execute, guided by the teams that know their customers best," said Kaz Ohta, CEO and Co-Founder, Treasure AI.

"The companies that thrive won't be the ones that replace their teams with AI. The winners will be those who learned to harness AI to drive 10x ROI in 10 minutes with the same human warmth and creativity that built their brand in the first place," added Ohta.

Studio rollout

A central component of the platform is Treasure AI Studio, a conversational workspace that allows marketing and data teams to interact with AI agents using natural language.

Users can input instructions such as campaign briefs or customer journey queries. The system then converts these inputs into executable actions, including building audience segments, generating campaign content and launching cross-channel initiatives.

Treasure AI Studio operates across web, mobile, desktop and command-line environments. It connects directly to a company's customer data platform, enabling actions based on unified customer profiles rather than fragmented datasets.

Data foundation

The platform relies on first-party customer data as its primary input. This data feeds into what the company describes as an Intelligent Customer Data Platform, which maintains a continuously updated view of each customer.

This structure enables the system to respond to behavioural changes in real time, supporting ongoing campaign adjustments without manual intervention.

AI outputs are designed to remain visible and reviewable before activation. Users can validate logic, approve recommendations and maintain oversight of automated processes.

Governance layer

Treasure AI has embedded governance controls within its AI systems, including lifecycle management, compliance checks and mechanisms to reduce inaccurate outputs.

The platform also supports integration with existing enterprise systems or can operate within its own suite of tools covering engagement, personalisation, creative production and service workflows.

"AI is no longer an assistive layer - it is becoming the primary interface through which work gets done. Yet AI cannot continuously find what matters about your customers, build content that performs, and launch campaigns in real time without a trusted system of record, where context, governance, and intelligence meet," said Rafa Flores, Chief Product Officer, Treasure AI.

"Where legacy marketing clouds have failed, Treasure AI is stepping up with the AI-native approach this market demands. We're here to help brands reduce costs by up to 50%, move at the speed of their customers, and capture value the moment it appears," added Flores.

Skills engine

Treasure AI Studio includes more than 50 pre-built skills that allow the system to operate on customer data rather than only analyse it.

These skills cover areas such as data queries, audience segmentation, campaign orchestration, workflow automation and analytics.

The platform is available to existing customers through a usage-based model. Each unit of AI usage allows multiple interactions within the Studio environment, enabling teams to scale activity according to demand.

Customer feedback cited early use cases involving automation of segmentation, campaign development and operational efficiency improvements.