Maze is a user research platform that the company positions as bringing recruiting, testing, and analysis together to help teams turn research into a continuous learning system and make decisions in a more evidence-based way. Its core proposition is "research at the speed of change," letting continuous user and market research support every decision.
In terms of use, Maze is aimed at teams that need to conduct user research and market research, covering the full chain from building market awareness, tracking change, and challenging assumptions to executing research. The company description mentions that teams can use it to build a shared understanding of customers, markets, and categories, providing an evidence-backed starting point for strategy; continuously observe how customer needs, market dynamics, and category signals change over time; and surface unanswered questions and untested assumptions behind roadmaps, meetings, and go-to-market plans, identifying the gaps most worth investigating.
Main capabilities include: connecting every research method, team, and stage so that learning can keep up with the decisions that depend on it; providing qualitative and quantitative research capabilities to test ideas, prototypes, messaging, customer needs, and market opportunities; AI provides assistance, but the user provides the judgment needed for trustworthy research; turning research results into clear reports, clips, and stories that teams can act on; Maze MCP can bring research into the tools teams use every day, so insights are discovered, reused, and applied to decisions.
For participant recruitment, the company says it can instantly tap into its panel of more than 6 million participants, or connect to your own community. Maze handles precise targeting and scheduling so teams can focus on asking smarter questions without worrying about logistics. On research methods, the company mentions that methods can be chosen according to the decision at hand, covering usability testing, interviews, concept testing, and market research, among others.
In terms of how it is used, the materials explain that teams can run research through the platform, recruit participants, conduct research, and bring evidence into decisions, then feed what they learn back into strategy, with each cycle building a stronger baseline for the next. Maze MCP is used to bring research into the tools teams use every day. Specific operational steps and interface flows are not yet provided in the available materials.
Use cases include: teams that want to build a shared understanding of customers, markets, and categories; teams that need to continuously track changes in customer needs, market dynamics, and category signals; teams that need to challenge untested assumptions in roadmaps, meetings, and go-to-market plans; teams that need to conduct usability testing, interviews, concept testing, and market research; and teams that want to bring research evidence into their everyday decision-making tools. The company also mentions that its customers include research teams that do not want to rely on guesswork, and lists some user roles, such as product leads, senior directors of UX and product innovation, and UX researchers.
LimitationsRegarding security, the official statement says that all traffic, including customer data, is transmitted securely and encrypted via SSL; passwords can be set for tests, and roles can be assigned to view, manage, and collaborate on research; the platform leverages AWS's comprehensive security measures to safeguard data security and high service availability; it protects data in accordance with GDPR standards and helps users comply easily as well; it supports verifying account access through single sign-on (SSO) to reduce security risks; it supports role-based workspace access, so that only specific team members can collaborate.
Regarding pricing, fees, specific platform support, details of AI features, the geographic distribution of participants, the number of research templates, and so on, the available information does not yet provide this. The official page also mentions insights from more than 200 research professionals and industry experts, covering how teams are responding to growing demand, AI adoption, and changes in the research function, and lists statements such as research can obtain user feedback in 1-2 days rather than weeks, annual research costs are reduced by 75%, and research capability and speed are increased by 2x, but these are promotional descriptions, and the specific metric definitions and applicable conditions are not yet provided.