GWI Released New Agentic AI Data Portfolio
The platform connects massive consumer datasets to AI assistants like ChatGPT, Claude, and Microsoft Copilot.
Updated on Oct. 12, 2026 in Artificial Intelligence
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GWI has launched a new agentic portfolio and ad-selling workflow designed to integrate deep consumer insights with AI assistants. The system allows users to leverage natural language to query data for pitch-ready narratives.
Why it matters
By connecting existing AI infrastructure to behavioral data, the system simplifies complex data interpretation for business users without technical expertise.
The suite utilizes the Model Context Protocol or Spark API to connect to AI models, drawing from a database of 45 billion verified datapoints. This information is derived from 17 years of behavioral data collected across more than 100 countries.
The players
GWI
Founded in 2009, the company specializes in providing consumer data and audience insights for global businesses.
The details
The portfolio allows non-experts to build professional-grade pitches by querying consumer attitudinal and behavioral data through AI agents. The workflow is compatible with major large language models including ChatGPT, Claude, and Microsoft Copilot.
Timeline
GWI was founded in 2009.
The new agentic portfolio and workflow were launched on October 12, 2026.
The Tech Race
The adoption of the Model Context Protocol marks a shift toward standardized interoperability between proprietary business databases and generative AI assistants. This move positions GWI to compete in the enterprise market by bridging the gap between legacy consumer data and modern agentic AI workflows.
Business professionals and marketers can now access complex audience insights through natural language prompts, reducing the time required to develop data-driven sales pitches. The compatibility with widely used tools like Microsoft Copilot means users can integrate these workflows into their existing software environments.
The takeaway
The move demonstrates how proprietary historical datasets are becoming the primary value driver for AI integration in the corporate sector. Professionals should prepare to shift from manual database querying to natural language interactions as standard industry practice.
Further reading
For more background on how companies are integrating machine learning into enterprise systems, explore our Artificial Intelligence section.
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