General Evidence AI Research Agent uses parallel AI roles to analyze questions against a behavioral panel and produce reports with sources, visualizations, and recommendations. Its team includes data analysts, data scientists, web researchers, and report writers. The panel contains more than 11 billion behavioral data points from over 29,000 opted-in people, verified through connected accounts. Coverage focuses on Germany and includes observed transactions, receipts, item-level grocery purchases, searches, browsing, video and social activity, apps and subscriptions, places, and demographics. The agent works with behavior already captured rather than waiting for a new survey fielding cycle. Users can ask follow-up questions to refine an analysis and explore a report further. General Evidence presents the product for CX and eBusiness, strategy and market research, brand marketing and media, category management, consumer insights, CRM and retention, and social and influencer marketing teams. The company says panel participation is reversible, connected sources can be disconnected, data is processed in the EU, and panel data is not used to train open models. Customers receive analysis, reports, and platform access rather than raw data. Pricing is available on request.
Who it is for
The Research Agent suits business teams needing Germany-focused behavioral research, sourced reports, visualizations, and recommendations. It is relevant to market research, marketing, consumer insights, CRM, and strategy groups.
What is good
- Parallel specialist AI agent roles
- Reports include sources and visualizations
- More than 11 billion panel data points
- Follow-up questions refine analyses
- Opt-in, reversible panel participation
What to know first
- Panel focuses on Germany
- Not a substitute for a global tracker
- Raw data is not provided
- Pricing is available on request
Verdict
General Evidence AI Research Agent turns an existing German behavioral panel into sourced, follow-up research reports. Its geographic scope and analysis-only delivery are important considerations for teams seeking broader coverage or raw datasets.
Compared on AI research assistants
- Source citations
- Yes
- Deep research mode
- Yes
- Team workspace
- Yes
