Research that accumulates, not scatters.

Interview summaries generated.
Every recorded research session is transcribed, and key findings, quotes, and observations are extracted into a structured summary. The researcher focuses on the conversation. The documentation happens after.

Themes identified across sessions.
Fabric reads across all your interview summaries and identifies recurring themes, patterns, and contradictions. Individual sessions become synthesized findings without anyone manually coding transcripts or building affinity diagrams.

Participant insights organized.
Each participant's session is documented individually, but their insights are also aggregated by theme, feature, or problem area. A finding mentioned by one participant is connected to similar findings from others, building a picture of how widespread an issue or need is.

A living repository, not a static report.

Findings update with every session.
Your research repository is not a report written at the end of a study. It updates continuously as new sessions are conducted. Five interviews in, you have emerging themes. Twenty interviews in, you have robust patterns with supporting evidence from dozens of participants.

Connected to product decisions.
Research findings are linked to the product documentation they inform. When a feature decision cites user feedback, the connection goes both ways. Product docs reference research. Research docs reference the decisions they influenced.

Searchable by anyone on the team.
Designers, engineers, product managers, and leadership can all search the research repository and ask the AI assistant questions about user feedback. Research stops being something locked in a researcher's files and becomes shared organizational knowledge.

The research synthesis bottleneck.

User research produces enormous amounts of raw material. A single hour-long interview generates thousands of words of transcript, dozens of observations, and multiple potential insights. Multiply that across ten, twenty, or fifty sessions and the volume becomes unmanageable. The research itself is not the bottleneck. The synthesis is. Reading through transcripts, identifying patterns, coding findings, building affinity maps, and writing up results takes longer than conducting the research in the first place.

This is why research backlogs exist. Teams conduct sessions faster than they can synthesize them. Findings from last month's study are still being written up when the next round starts. By the time the report is finished, the product has moved on and the findings feel stale. Valuable insights sit in unprocessed recordings that nobody has time to review. The research was done. The knowledge was generated. But it never made it into a format that anyone could use.

Synthesis that happens automatically.

Fabric eliminates the synthesis bottleneck by processing research sessions as they happen. Record an interview and the transcript is generated automatically. Key findings, notable quotes, and participant observations are extracted into a structured summary. As more sessions are processed, Fabric identifies themes that recur across participants, contradictions between different users, and patterns that strengthen or weaken with additional data.

The output is not a raw dump of everything that was said. It is structured research documentation organized by theme, by participant, and by relevance to specific product areas. A product manager can look up what users have said about a specific feature. A designer can search for pain points related to a particular workflow. An engineer can understand the user context behind a requirement. The research is accessible to the people who need it, in a format they can use, without waiting for a formal report.

Research memory that spans studies.

Individual research studies are valuable, but the real power of a research repository comes from accumulation across studies over time. A usability issue flagged in a study six months ago gains significance when it surfaces again in a different context. A user need identified in early discovery research connects to feedback from a beta study conducted months later. These longitudinal connections are nearly impossible to maintain manually because they require someone to remember findings from previous studies and connect them to current ones.

Fabric maintains these connections automatically. Findings from a new session are compared against the existing repository. When a theme or insight echoes something from a previous study, the connection is documented. Over months and years of research, you build a repository that represents your organization's complete understanding of your users, not just what you learned in the most recent round. For research teams conducting ongoing research programs, this longitudinal memory is transformative.

From researcher's files to shared knowledge.

Research findings are most valuable when they reach the people making decisions. But in most organizations, research lives in the researcher's files, shared through slide decks that get presented once and then forgotten. The people who need the findings most, product managers making feature decisions, designers choosing between approaches, engineers understanding user context, have to ask the researcher directly or hope the relevant deck is findable somewhere.

Fabric's research repository makes findings available to the whole team through search and the AI assistant. A product manager preparing a spec can search for user feedback on the relevant problem space. A designer can ask the AI assistant what users have said about a particular interaction pattern. The research is not locked behind a presentation. It is part of the team's shared knowledge base, connected to product docs and decision records that reference it.

What gets produced.

Session summaries
Structured summaries of each research session with key findings, notable quotes, participant profile, and observations. Generated automatically from recorded sessions and researcher notes.

Theme analysis
Recurring themes identified across multiple sessions with supporting evidence from individual participants. Themes strengthen or shift as new data comes in from additional sessions.

Participant profiles
Individual participant pages documenting their feedback, insights, and notable quotes across all sessions they participated in. Useful for referencing specific user perspectives.

Feature-level feedback
User feedback organized by feature or product area. What users have said about a specific feature across all studies, aggregated in one place.

Research timeline
A chronological record of all research activity showing when studies were conducted, how many participants were involved, and what major findings emerged from each round.

Use cases

Ongoing product research
Build a research repository that grows with every session. Emerging themes become robust patterns as evidence accumulates across participants and studies. See how Fabric supports research.

Usability testing
Session recordings are transcribed and findings are extracted automatically. Usability issues are tracked across participants and connected to the product areas they affect.

Customer discovery
Early-stage research where patterns are still emerging. Fabric identifies themes as they develop and strengthens or challenges them with each new conversation. Findings feed directly into product documentation.

Academic and qualitative research
Students and researchers conducting interview-based studies get automatic transcription, thematic analysis, and organized findings. The synthesis step that usually takes weeks happens as sessions are conducted. See how Fabric supports dissertations.

Perfect for

User researchers
Spend your time conducting research rather than synthesizing it. Fabric handles transcription, summary generation, and thematic analysis so you can focus on the conversations that produce the insights. Learn more about Fabric for user research.

Product managers
Access user feedback organized by feature and theme without waiting for a formal research report. Search for what users have said about the problem you are working on and get answers immediately. Learn more about Fabric for product managers.

Research teams
Build a shared repository that spans researchers, studies, and time periods. New team members can search the full history of your organization's research rather than starting from scratch. Learn more about Fabric for research teams.

Startups without dedicated researchers
When everyone on the team does some user research, having a central repository that organizes findings automatically is especially valuable. No one person needs to own the synthesis. Learn more about Fabric for startups.

Works seamlessly with other features.

Product docs
Research findings are connected to the product documentation they inform. Feature decisions that cite user feedback link back to the research that generated it.

Audio and video transcription
Research sessions are transcribed automatically with timestamped, searchable output. The AI assistant can cite the exact moment a participant said something specific.

AI assistant
Ask your AI assistant questions about your research. "What have users said about onboarding?" "How many participants mentioned pricing as a concern?" It searches across your entire repository and cites specific sessions.

Annotations
Annotate session transcripts and summaries with your own observations. Your manual notes are preserved alongside the automatically generated analysis.

FAQ

What sources does Fabric use to build the research repository?
The repository is assembled from recorded research sessions, researcher notes in your workspace, and related discussions in Slack or Discord. Each source contributes different types of context.

Does Fabric replace manual qualitative analysis?
Fabric automates transcription, summary generation, and initial thematic analysis. Researchers can review, refine, and deepen the analysis that Fabric produces. It handles the time-consuming first pass so researchers can focus on interpretation and insight.

How does Fabric identify themes across sessions?
Fabric reads across all session summaries and identifies topics, observations, and feedback that recur across participants. Themes are presented with supporting evidence from individual sessions and strengthen as more data confirms them.

Can I search for what users said about a specific feature?
Yes. The research repository is searchable by feature, theme, or keyword. You can also ask your AI assistant to aggregate everything participants have said about a specific topic across all sessions.

Does the repository update as I conduct more sessions?
Yes. Every new session adds to the repository. Themes are re-evaluated, new patterns emerge, and existing findings are strengthened or challenged by additional data. The repository is a living document, not a static report.

Can I see the original transcript behind a finding?
Yes. Every finding links back to the session summary and full transcript it came from. You can click through to the specific timestamp in the recording where the participant made the relevant comment.

Can non-researchers access the repository?
Yes. The repository is part of your Fabric workspace with whatever permissions you set. Product managers, designers, engineers, and leadership can all search and reference research findings.

How does this handle participant privacy?
You control what information is included in the repository. Participant names can be anonymized, and access to the repository can be restricted using granular permissions and password protection.

Can I use this for academic research?
Yes. The same capabilities apply to academic qualitative research. Interview transcription, thematic analysis, and organized findings work for dissertation research, literature reviews, and any interview-based study. Learn more about Fabric for students.

Which plans include the user research repository?
Self-writing user research docs are available on Team plans. See team pricing for details.