Sprig Feedback

Sprig's core proposition is context. Most survey tools hand you a CSV of opinions; Sprig hands you the opinion plus a session replay clip of up to 5 minutes before and after the answer, and it lets a study fire off a behavioral event rather than a date in a calendar. That combination is genuinely hard to assemble yourself, and it is what separates it from embedding a form widget in your app. Strengths. Installation is once — Web, iOS, Android, React Native, Flutter — and after that research teams adjust targeting and wording without engineering tickets. Questions can reference live user attributes and recent behavior, so a participant sees something aligned to what just happened. The agent layer is unusual in this category: Design structures studies, Field runs adaptive studies at scale, Analyze produces statistical analysis, and Synthesize produces reports, which shrinks the gap between collecting 3,000 open-ended answers and telling the org what they mean. Distribution is omnichannel from one study — in-product, email, link, and a panel of 300K+ verified respondents — and the MCP server lets Slack, Notion, or Claude pull research data, which is what makes continuous monitoring possible instead of one-off studies. Governance is enterprise-shaped: SSO, role-based access controls, HIPAA, SOC 2 Type II, GDPR, CCPA. Weaknesses. The install is heavier than a snippet-and-go widget tool, and someone with front-end skills has to own it. Pricing is not published as a tier list — the vendor quotes it, and it scales with total response volume across studies, which makes forecasting awkward if you run many small studies instead of a few large ones. Sprig also does not disclose which underlying AI models power the agents, so you cannot audit that part of the stack. And it is not a fit for offline or in-person fieldwork, or for projects that cannot embed a JavaScript SDK for architecture or security reasons. Where it fits. Product teams running continuous discovery who need to know why a user answered, not just what they answered. UX researchers who want the agent layer to absorb study design, fielding, and synthesis work. Enterprise research organizations consolidating a pile of disconnected survey tools into one governed system with access controls across multiple teams and products. Where it does not. A small team that just wants a popup NPS question, a researcher whose work happens face-to-face, or anyone who needs to see a published price before they will sit through a demo call.