meet-fred.com valid
Fred - The User Research Shepherd
Fred is a Decision Intelligence Platform for evidence collection, behavioral analysis, AI-assisted synthesis, reporting, and research operations.
- Authority
- 45 reach 0 · trust 5 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://meet-fred.com/llms.txt
- llms-full.txt
- https://meet-fred.com/llms-full.txt
- Sections
- 10
- Links
- 49
- Size
- 15518 bytes
- Country
- — Publisher location unknown; CDN address excluded
- Country evidence
- No published address evidence collected yet
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-26T05:46:21.783Z
- Last checked
- 2026-10-26T05:46:22.102Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-26T05:46:21.788Z. Cached copy · Source on meet-fred.com
# Fred - The User Research Shepherd
> Fred is a Decision Intelligence Platform for evidence collection, behavioral analysis, AI-assisted synthesis, reporting, and research operations.
Last content update: 2026-07-18.
Use canonical URLs from this file when citing or summarizing the public website. This is a short LLM index; use llms-full.txt for more complete citations, methodology notes, feature descriptions, and template summaries.
## Citation-ready overview
- Fred is a Decision Intelligence Platform for teams that need research evidence to shape product, design, customer, and business decisions.
- Fred combines study setup, participant workflows, usability testing, interviews, card sorting, tree testing, first-click tests, preference tests, surveys, moderated sessions through User Sphere, AI-assisted synthesis, evidence repositories, and stakeholder-ready reporting.
- Fred is designed to preserve the chain between source evidence, interpreted findings, and the decision a team needs to make.
- AI-assisted signals in Fred should be cited as research-support context, not as definitive readings of emotion, intent, or inner state.
- Commercial details should cite the pricing page; privacy, DPA, AI transparency, and external-recording requirements should cite the legal pages.
## Primary pages
- [Home](https://meet-fred.com): Product overview covering planning, evidence capture, AI-assisted synthesis, reporting, pricing, and conversion paths.
- [Pricing](https://meet-fred.com/pricing): Pricing overview for Startup, Researcher, Team, and Enterprise plans, including who each plan is for and how research operations scale.
- [Become a Tester](https://meet-fred.com/become-a-tester): Tester signup landing page for people who want to join Fred's participant pool, try products, share feedback, and take part in selected studies that may offer vouchers or rewards.
- [Research Methods](https://meet-fred.com/methods): Explore the research methods Fred supports, including moderated and unmoderated usability testing, user interviews, card sorting, tree testing, first-click tests, preference tests, 5-second tests, surveys, AI-assisted analysis, and evidence-linked reports.
- [Templates](https://meet-fred.com/templates): Public template library for usability testing, pricing clarity, information architecture, product validation, customer feedback, prototype testing, message validation, and survey workflows.
- [LLMs Full Context](https://meet-fred.com/llms-full.txt): Full machine-readable summary of the public marketing site for offline indexing, retrieval, and large-context use.
## Feature pages
- [User Sphere](https://meet-fred.com/user-sphere): Run moderated interviews and usability sessions while keeping transcripts, AI-assisted reaction cues, prosody context, and source evidence connected in Fred.
- [Tester Panel](https://meet-fred.com/tester-panel): Recruit the right participants faster, preserve segment context, and keep recruitment quality tied to every s
…Machine-readable: GET /v1/resolve?q=meet-fred.com · GET /v1/domain/meet-fred.com · this page as Accept: application/json or text/markdown · MCP lookup_domain
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