GWI offers three ways to work with our data, depending on your workflow.
  • Platform API exposes structured variables and tables for BI and modeling.
  • Spark API returns concise, narrative insights from natural-language or structured queries.
  • Spark MCP (via Spark) brings those insights directly into AI assistants and agent workflows. The table below outlines purpose, interaction style, output format, typical users, setup effort, and best-fit scenarios for each option.
FeaturePlatform APISpark APIMCP (via Spark)
PurposeExtract structured data for custom analysis and dashboardsRetrieve summarized, ready-to-use insights via queriesIntegrate GWI insights directly into AI assistants and workflows
Interaction modeStructured API requests (parameter-based)Natural language prompt or structured requestNatural language, machine-readable query translation
Insight formatRaw variables and tablesSummarized insights (e.g. X%s of Gen Z prefers Y)Textualized outputs designed for LLMs/Copilots
Use case examplesFeed GWI data into BI dashboards, tools, or modelsAsk questions and get insights about your specific audience to help with content planning or persona buildingEnable copilots to answer audience questions and use these results to shape AI model outputs - whether generating text, creating imager, or guiding the actions of agents and other MCPs
End userData analysts, data engineersPMMs, researchers, content strategistsProduct, AI, innovation teams using assistants or LLM tools
Setup effortHigh (requires integration and data handling)Low (requires some understanding of query structure)Low (using natural language)
Best forBulk analysis, exploration, custom visualizationsSelf-serve insight discovery without logging into the platformReal-time, context aware audience intelligence inside other tools, agentic platforms and workflows.
Common tools integrated withTableau, PowerBIInternal tools, campaign planningChatbots, copilots, AI agents, Claude