Fabric for product design agencies

Product design is research-heavy. Before the first sketch, there's competitive teardowns, market analysis, user interviews, material research, and manufacturing constraints. During development, there's user testing, prototype iterations, supplier conversations, and stakeholder feedback. The research informs the design, but the research is in one set of tools and the design process is in another, and the connection between "why we chose this material" and the actual material decision lives in someone's memory.

Fabric keeps the entire product development context searchable. Competitive teardowns, user testing notes, material specs, supplier conversations, and design rationale, all in one workspace. When the client asks "why did we choose this direction," the answer is one search away, not a reconstruction from memory.

Research connected to decisions

AI search reads inside every research document, interview transcript, and meeting note and searches by meaning. "The competitive teardown of the Samsung product" finds the research. "What users said about the grip in testing" finds the interview moments. "The material spec from the supplier in Shenzhen" finds the email.

The AI assistant synthesises across the full project record. Ask it why a design decision was made and it pulls from the research, the meeting where it was discussed, and the testing that informed it. Self-writing docs produce decision logs that capture the reasoning from the conversations where decisions happened.

User testing findings that stay active

User interview recordings are transcribed and searchable. Testing results are findable alongside the prototypes they evaluated. Annotations let the team mark up test results and prototype photos with observations. The findings inform the next iteration because they're findable, not because someone remembered to reference them.

Competitive analysis that compounds

Every competitive teardown, every market analysis, every product comparison deepens a searchable library. When a new project starts in the same category, the past research is one search away. The explorer surfaces connections across projects.

Supplier and stakeholder communication

Forward supplier emails to email-to-note. Record stakeholder calls with AI voice notes. Material specs, pricing, and lead time discussions are searchable alongside the design decisions they inform. Publish deliverables with link analytics.

For the full agency workflow, see Fabric for agencies. For managing design projects, see design projects. For competitive research, see competitive research.

Get started

Keep product development context searchable from research to production. Try Fabric free. See pricing for teams.

FAQs

Can I search across competitive teardowns and user testing?

Yes. AI search reads inside every document and transcript and searches by meaning.

Can the AI explain why a design decision was made?

Yes. The AI assistant draws from research, meetings, and decision logs.

Can I search user interview transcripts?

Yes. Audio and video transcription transcribes interviews. Search by what was said.

Can I track supplier conversations?

Yes. Forward emails to email-to-note. Record calls with AI voice notes.

Do decision logs capture the reasoning?

Yes. Self-writing decision logs capture what was decided and why from meetings and Slack.

Can past research inform new projects?

Yes. Search across every project. Competitive research and user testing findings compound across engagements.

Can I share deliverables with tracking?

Yes. Publish with password protection and link analytics.

What happens when someone leaves the project?

Full context remains searchable. Research, decisions, and client preferences are in the system.

Is client data secure?

Yes. AES-256 encryption, CASA Tier 2 compliant. Data never used for model training.