Introduction
Sprig is an enterprise survey platform powered by AI agents. Research, product, marketing, and customer experience teams use it to design studies, field them across in-product, email, link, and panel channels, and turn responses into evidence. AI agents remove the manual work between each step, and a human reviews and approves every study before it reaches anyone. Sprig supports customer research, market research, and in-product research in one platform and connects to other tools through integrations, APIs, and a Model Context Protocol server.
Sprig exists due to a timing problem. AI has made production cheap—code, designs, campaigns, and product concepts all ship faster than they did two years ago—but learning from customers has not kept pace. A traditional enterprise survey runs on a specialist's calendar, where someone programs the instrument, someone else buys the sample, fielding takes a week or two, and the analysis arrives after the decision it was meant to inform. Sprig compresses that cycle. The aim is a shorter time to insight, not a thinner set of research methods.
Key Takeaways
Sprig combines study design, distribution, and analysis in one AI-agent workflow. Here are the essentials:
- Sprig is an enterprise survey platform powered by three AI agents (Design, Field, and Synthesize) that span study design, fielding, and analysis.
- It supports six study types and a documented set of question types, including advanced methods such as MaxDiff and Conjoint on the Enterprise plan.
- Studies reach people through in-product surveys (web and native mobile), email, shareable links, QR codes, iFrames, and external research panels.
- An agent can draft a study, but a human reviews and approves every study in the Sprig app before it reaches a respondent.
- Sprig holds SOC 2 Type II and meets HIPAA, GDPR, and CCPA requirements, and that response data is not used to train models.
- Sprig fits teams that want customer and market research in one platform with lower operational lift. It is not a voice-of-customer suite, a moderated-interview tool, or an offline fieldwork vendor.
How Does Sprig Work?
Sprig runs three named agents across the research lifecycle. The Design Agent builds a programmed study, the Field Agent delivers it adaptively, and the Synthesize Agent turns responses into themes and recommendations. The agents remove manual work between a question and an answer, rather than replacing research expertise.
The Design Agent builds a programmed study from a document, a research goal, a brief, or an existing questionnaire, with response options, skip logic, and randomization already configured. Before anyone fields it, the agent flags unclear questions, detects conflicting logic, and estimates completion time.
The Field Agent delivers adaptive studies, personalizing questions and generating follow-ups in real time based on what a respondent has already said. AI follow-ups can be switched off per study, so teams that need a fixed instrument can keep one.
The Synthesize Agent turns responses into reports, themes, and recommendations as they arrive. Every theme carries participant quotes, with traceability back to the responses underneath it, so a finding can be checked against the evidence it came from.
| Agent | What it does | What a human still does |
|:---:|:---:|:---:|
| Design Agent | Builds a programmed study with response options, skip logic, and randomization configured; flags unclear questions, conflicting logic, and completion time | Reviews and approves the study before launch |
| Field Agent | Delivers adaptive studies, personalizing questions and follow-ups from prior answers (AI follow-ups can be turned off) | Sets targeting rules and approves what goes live |
| Synthesize Agent | Produces reports, themes, and recommendations, with quotes and traceability to the underlying responses | Interprets findings and decides what to act on |
What Can You Run on Sprig?
Sprig supports six study types and a documented set of question types, plus study logic for building rigorous instruments.
The six study types are:
- Surveys
- In-Product Surveys
- Feedback
- Replays
- Heatmaps
- Prototype Testing
Questions range from rating scales to advanced methods such as MaxDiff and Conjoint, and several advanced methods are reserved for the Enterprise plan.
Fourteen question types are documented, including:
- Rating Scale
- Open Text
- Multiple Choice
- Matrix
- Rank Order
- Net Promoter Score
- MaxDiff
- Conjoint
- Video and Voice
Recorded Task captures screen, voice, and video inside a prototype. Study logic covers skip logic, display logic, response piping, attribute piping, response-based quotas, AI translations, and randomization at three levels: response options, questions within a page, and pages within a survey.
| Layer | What Sprig documents |
|:---:|:---:|
| Study types (6) | Surveys, In-Product Surveys, Feedback, Replays, Heatmaps, Prototype Testing |
| Question types (14 documented) | Rating Scale, Open Text, Multiple Choice, Matrix, Rank Order, Net Promoter Score, MaxDiff, Conjoint, Video and Voice, Recorded Task, and others |
| Study logic | Skip logic, display logic, response and attribute piping, response-based quotas, AI translations, three-level randomization |
| Enterprise-plan methods | Rank Order, Recorded Task, MaxDiff, Conjoint (each restricted to particular study types) |
How Do Sprig Studies Reach People?
Sprig studies reach participants through six documented channels. In-product surveys run on web and on native mobile through iOS, Android, React Native, and Flutter SDKs. Studies also go out by email with a custom sending domain, through shareable links, QR codes, and iFrames, and through external research panels.
Panels can be filtered by more than 300 demographic, professional, behavioral, and firmographic targeting attributes, which lets teams reach people outside their existing customer base. Links can also be sent through a team's own email or SMS tooling. This range means that one platform can serve both in-product research, where the audience is current users, and market research, where the audience is a defined external segment.
Is Sprig Built for AI Agent Workflows?
Sprig runs a Model Context Protocol connector so agents in tools such as Claude, ChatGPT, Cursor, and Gemini can work with its research data. The governance model is published rather than implied: an agent can create studies in draft only, and a human reviews and approves every study in the Sprig app before it reaches anyone.
According to Sprig's published governance model, an agent can’t push a study live, change a running study, or reach respondents on its own. Permissions are checked at every tool call rather than once at connection time, calls are capped at 1,000 responses, and admins can revoke every MCP connection across the organization from a single setting. Sprig states that response data is not used to train models, that the client never holds standing access to a workspace, and that it never receives a bulk export. Confirm these controls against current documentation before relying on them in a security review.
Who Uses Sprig, and Who Is It for?
Sprig is built for research, product, marketing, and customer experience teams that want customer and market research in one platform. Publicly referenced customers shown on Sprig's site include Figma, DoorDash, and Notion. Teams use it for customer surveys, market research, experience measurement, journey and behavioral research, concept and prototype testing, and strategic foundational studies.
Different buyers value different parts of the platform. User researchers and research operations care about rigor, governance, methodology, and defensible findings. Product managers want fast answers and low operational lift, with feedback tied to product workflows. Marketers run message testing, brand research, concept testing, and segmentation, often reaching an external audience. Data and technical teams care about the APIs, the MCP connector, and structured outputs they can wire into internal systems.
How Does Sprig Handle Security and Compliance?
Sprig holds SOC 2 Type II and meets HIPAA, GDPR, and CCPA requirements, with data access, erasure, and opt-out functionality available to Enterprise customers. Data is encrypted at rest, SDK and backend communication runs over SSL, and a zero-data-retention arrangement covers AI operations with OpenAI. Confirm the current certifications before relying on them.
Sprig does not implicitly collect personally identifiable information, which has to be sent explicitly through the data collection APIs, and that single sign-on runs through SAML. For a buyer, the practical read is that Sprig is positioned for enterprise procurement, but the specific certifications, sub-processors, and data-residency terms should be verified directly with Sprig during evaluation.
Where Does Sprig Fit, and Where Does It Not?
Sprig fits teams that need survey-based customer and market research, quickly, in one platform. It’s not a voice-of-customer suite, a moderated-interview tool, or an offline fieldwork vendor, and it doesn’t publish weighting or significance-testing methodology. Those are deliberate boundaries, not roadmap gaps.
| If you need | Sprig fit | Better served by |
|:---:|:---:|:---:|
| Survey-based customer, market, or in-product research, fast | Strong fit | Sprig |
| Advanced quant such as MaxDiff or Conjoint with in-product reach | Fit on the Enterprise plan | Sprig Enterprise |
| Moderated or AI-moderated live interviews | Not a fit; Sprig is asynchronous (recorded video and voice, recorded task) | A moderated research or interview platform |
| Contact-center, IVR, review-site, or social listening, or closed-loop case management | Not a fit | A voice-of-customer or customer experience management suite |
| Offline fieldwork, CATI, CAPI, or native SMS | Not a fit | A traditional fieldwork vendor |
| Documented weighting, sample-size guidance, and significance testing | Evaluate directly before choosing | A platform that publishes statistical-inference methodology |
How is Sprig Different From a Traditional Survey Platform?
Sprig differs from a traditional enterprise survey tool mainly in where the manual work sits. On most platforms, a specialist programs the instrument, buys the sample, waits a week or two to field, and analyzes after the decision. Sprig's agents compress that cycle, with less manual configuration at each step.
A highly complex global program with deeply customized legacy workflows may still prefer a broad experience-management suite, so buyers should consider comparing alternative platforms based on their goals and structures. Weigh the specific workflow differences that matter for your team: time to launch, in-product targeting, distribution channels, advanced method coverage, analysis workflow, administration, and agent and API support. Sprig maintains head-to-head comparison pages against named competitors for buyers who want that detail.
Who Created Sprig?
Sprig is made by Sprig Technologies. The company was founded in 2019 by Ryan Glasgow, who serves as CEO, and was known as UserLeap until it was renamed to Sprig in 2021. It is headquartered in San Francisco with an additional office in New York.
Ryan Glasgow started the company on the premise that product development had outrun the tools teams use to learn from customers. Around the company's Series B, he framed the problem as a decade of progress in product management while, in his words, "the product tech stack, and especially the qualitative research toolset, has not kept pace." Closing the gap between how fast teams ship and how slowly they hear back from customers is the job Sprig is built for, first through in-product surveys and now across a broader enterprise survey platform.
Sprig is a Series B company that has raised approximately $90 million to date, from investors including Andreessen Horowitz, Accel, First Round Capital, Elad Gil, and Figma Ventures. Andreessen Horowitz led both its $38 million Series B in June 2021 and its $30 million round announced in August 2022. For a buyer, the practical read is backing from established investors and enough capital to support enterprise deployment, though funding is not a substitute for evaluating the product against your own requirements.
Sprig began as UserLeap, an in-product survey tool, and relaunched as an all-in-one research platform in 2021. It has since broadened into a full enterprise survey platform, adding email, link, and panel distribution alongside the three research agents. This trajectory matters for buyers weighing direction: Sprig is moving from a single-channel product-research tool toward research infrastructure for both people and agents. Product documentation lives at docs.sprig.com.
On buyer-review sites, Sprig holds 4.3 out of 5 across 199 reviews in G2's Survey Software category and 8.5 out of 10 across 10 reviews on TrustRadius, as of September 2026.
Frequently Asked Questions
What is Sprig used for?
Sprig is used to run customer and market research from a single platform. Teams design a study, field it in-product or through email, links, or panels, and get themes and recommendations back from the Synthesize Agent, covering use cases from onboarding and feature feedback to concept testing and market research.
Is Sprig only for in-product surveys?
No. Sprig started with in-product surveys and now also supports email surveys, shareable links, QR codes, iFrames, and external research panels. That lets one platform serve both current users inside a product and defined external audiences for market research.
Does Sprig replace your research team?
No. Sprig's agents remove manual configuration and speed up synthesis, but a human sets the research objective, reviews and approves every study before launch, and decides what the findings mean. Sprig positions the agents as increasing speed and rigor, not as running research on their own.
Which Sprig features are Enterprise-plan only?
Rank Order, Recorded Task, MaxDiff, and Conjoint are documented as Enterprise-plan features, and each is restricted to particular study types. Confirm the current plan boundaries with Sprig, since packaging can change.
Can an AI agent launch a Sprig study on its own?
No. Through the Model Context Protocol connector, an agent can create a study in draft only. A human reviews and approves it in the Sprig app before it reaches any respondent, permissions are checked at every tool call, and admins can revoke MCP access organization-wide.
Does Sprig train AI models on my response data?
Sprig states that response data is not used to train models and that a zero-data-retention arrangement covers its AI operations with OpenAI. Verify the current data-handling terms and sub-processor list with Sprig during procurement.
Conclusion
Sprig is an enterprise survey platform powered by AI agents that design, field, and synthesize research in one workflow, with a human approving every study before it launches. It fits teams that want customer and market research together, with less manual setup, and it is deliberately not a voice-of-customer suite or a moderated-interview tool.
To evaluate fit, work through how the three agents map to your research process, then check pricing and plans to see which advanced methods your team needs.
