Sydney, Australia — Displayr, the AI-powered analysis and reporting platform for market researchers and insights teams, today announced the launch of Chat + Agents, a new built-in experience designed to help teams move through survey analysis without breaking their workflow.
In research and insights work, the challenge rarely ends with the first result. Teams need to test interpretations, explore segments, answer follow-up questions from stakeholders, and refine the story as understanding evolves. Yet most tools force work to restart at each step, requiring analysts to re-brief, rebuild analysis, or move results across systems. Context is lost, logic fragments, and decisions become harder to defend.
Displayr’s Chat + Agents are designed to solve this problem by keeping analysis, results, and conversation connected. AI agents automate the most time-consuming parts of research, including data preparation and analysis, while Chat allows teams to explore findings, challenge assumptions, and ask successive questions directly against the same live analysis.
Rather than operating as a conversational layer sitting on top of static dashboards or exported results, Displayr’s Chat works directly with the underlying analysis, using the same data, tables, and assumptions that power the results themselves. When analysis updates, chat reflects those changes automatically.
“Most AI tools give you answers without taking responsibility for how they were produced,” said Tim Bock, Founder and CEO of Displayr. “We took a different approach. Chat works on the actual analysis, not a copy of it. That means researchers can see what’s happening, challenge it, change it, and trust it when they take it into decision-making.”
Chat is designed to work alongside Displayr’s analysis and data preparation agents, which automate the most time-consuming parts of research using the same methods teams already trust.
The Research Agent applies established analytical logic to determine appropriate analyses for a given research question, generate tables and visualizations, and structure results, while the Data Preparation Agent handles data cleaning, transformations, and quality checks. Because these agents create real, inspectable analysis inside Displayr, Chat can engage directly with their outputs, allowing teams to explore, refine, and extend the work without rebuilding or losing context.
