Sprig is an enterprise research platform whose core proposition is "enterprise research, agent-driven," transforming traditional manual questionnaire workflows with an AI agent system so enterprise teams can move faster from asking questions to defensible insights without sacrificing rigor.
Purpose and Positioning Sprig is built for enterprise research scenarios and supports experience measurement, foundational discovery, journey and behavioral research, and market and consumer insights. It emphasizes that "research is rebuilt around agents," shifting the research process from "programming" to "directing," that is, reducing the work of manually building questionnaires.
Main Capabilities
- Design Agent: It can skip manual questionnaire building and directly upload questions, letting the agent construct the research; after describing the goal, the agent builds logic, branching, and question flow to ensure data quality; automatically configures complex skip logic, identifies broken flows, and ensures a smooth participant experience; conducts AI bias audits, identifies and corrects leading questions, ensures neutral wording, and optimizes question selection to reduce participant fatigue and maximize signal.
- Field Agent: It enables adaptive research rather than static forms; adjusts the questionnaire based on how participants answer and who they are; delivers rigorous research through a conversational interface, making completion more natural and easier; generates personalized questions based on goals or adapts through real-time follow-ups; an attribute pipeline can insert metadata directly into the flow so each participant sees questions relevant to their journey.
- Analyze Agent: It provides advanced analysis in plain language; recommends suitable statistical methods for the research, from crosstabs and t-tests to ANOVA and correlation, and runs these methods; can adjust and rerun analyses using natural language, then move results directly into Sprig reports.
- Synthesize Agent: It turns structured and open-ended results into presentation-ready narratives aligned with the research goals; generates research reports that can be edited and shared with stakeholders; detects patterns across segments and presents non-obvious themes supported by data; refines findings, adjusts evidence, and validates conclusions before sharing.
- Integrations and Connections: Sprig can be connected to AI tools to process data directly without exporting or manual setup.
How to Use Users can upload questions to let the Design Agent generate research, or describe goals to let the agent build logic, branching, and question flow; during deployment, they can share links, trigger lifecycle campaigns, or recruit target groups; surveys can be deployed directly within Web, iOS, and Android apps; participants complete adaptive questionnaires in a conversational interface; segmentIn the analysis phase, use natural language to adjust and rerun statistical methods; finally, generate a report and share it.
Applicable scenarios Supports research across the full spectrum of enterprises: experience measurement (monitoring health metrics, with agents explaining the causality behind score changes), foundational discovery (discovering product-market fit and strategic opportunity areas), journey and behavior research (identifying friction and churn in product flows), and market and consumer insights (rigorous research to understand markets, audiences, and opportunity areas). Enterprise research requires omnichannel reach; Sprig supports distribution via email, panel, website, and mobile app from a unified research platform.
Limitations and other information The official text does not provide specific pricing, charging models, platform availability details, independent performance data, or technical implementation limitations; this information is not currently available.
