AI User Testing, Surveys & Interviews Platform | TheySaid

What

TheySaid is an AI-powered feedback platform for running user testing, surveys, interviews, forms, polls, and pulse checks in one system. It is positioned as a unified alternative to traditional feedback workflows that often require separate tools for recruiting, moderation, analysis, and reporting.

The platform appears designed for teams in product, UX research, marketing, design, sales, HR, operations, engineering, and founder-led organizations that need faster qualitative insight. Its core workflow centers on conversational feedback collection, AI-generated follow-up questions, and real-time analysis to uncover not just what users say, but why they say it.

Features

• AI user testing: Runs usability and experience tests with AI follow-up questions to probe deeper into user reactions and behavior.
• AI-moderated interviews: Automates interviews with dynamically adapting questions, helping teams scale qualitative research without manual moderation.
• Conversational surveys and forms: Replaces static questionnaires with guided, adaptive flows that can collect more detailed responses.
• AI polls with explanation capture: Goes beyond simple voting by asking respondents why they chose an answer and summarizing the reasoning in real time.
• Two-way voice interaction: Supports natural spoken feedback where AI listens, responds, and adjusts the conversation as it progresses.
• Real-time insight support: Includes AI analysis and sidebar guidance during feedback sessions to help teams interpret responses as they come in.

Helpful Tips

• Validate depth of analysis in a live workflow: If evaluating this type of platform, test whether the AI follow-up questions consistently produce useful qualitative detail rather than repetitive probing.
• Map the tool to a specific research motion first: Adoption is usually easier when teams start with one use case such as win-loss analysis, user testing, or employee feedback before expanding broadly.
• Check channel and workflow fit carefully: The site mentions delivery through existing channels and workflow connectivity, but details are limited and some workflow connections are marked “coming soon.”
• Define internal review rules for AI-generated summaries: Real-time summarization can speed decisions, but teams should still verify key themes and edge cases before acting on insights.
• Use role-based ownership: Because the platform spans many departments, assigning clear owners for research design, analysis review, and insight distribution can reduce duplication and confusion.

OpenClaw Skills

TheySaid could likely work well in an OpenClaw environment as the feedback capture layer for customer insight workflows. Likely skill patterns include an agent that ingests TheySaid interview or survey outputs, clusters themes by product area or customer segment, drafts research summaries for stakeholders, and routes findings to product, marketing, or sales teams. If the platform exposes structured outputs or workflow hooks, OpenClaw agents could help turn raw conversational feedback into repeatable operating processes.

A stronger combined use case would be profession-specific insight automation. For product teams, an OpenClaw agent could likely convert TheySaid findings into backlog recommendations, hypothesis briefs, or usability issue reports. For sales and marketing, it could transform win-loss interviews and deal recovery insights into objection maps, messaging revisions, and campaign inputs. These are likely workflow extensions rather than confirmed native integrations, but together they could shift feedback-heavy teams from periodic manual research toward continuous, AI-assisted decision support.