Each part of the product solves a specific problem teams run into when user feedback piles up faster than it gets used.
Every conclusion traces back to the original quote.
AI suggests, you approve — the chain stays auditable.
Quantified strength of evidence, from −10 to +10.
Each fact anchored to the research context it came from.
Widget, CSV, API, or manual entry — wherever it lives.
AI-written answers with every source cited inline.
Roles, permissions, and full data export — yours to keep.
Every recommendation connects back to a real quote from a real user.
Recommendations link to the hypotheses behind them. Hypotheses link to the facts that support or weaken them. Facts link to the original feedback they came from. When someone asks why you're prioritising something, you can show the chain — not just defend the call.
The reading and tagging you'd do if you had a week — done in the background, always reviewable.
Vyrdis breaks each piece of feedback into atomic claims, suggests tags, and matches new feedback to your existing hypotheses by meaning rather than wording. Patterns that get lost when you skim a hundred responses show up where you can act on them. Every step is visible and reversible.
Write the theory you came in with, and watch the evidence build for or against it. Or let Vyrdis surface patterns from your feedback you hadn't spotted.
If you have a hunch — say, "users avoid the new checkout flow" — write it down and Vyrdis searches your facts for what supports or weakens it. Each hypothesis gets a strength score from −10 to +10: supporting facts raise it, counter-evidence lowers it, and newer feedback counts for more. If you're starting from scratch, let the AI cluster patterns in your feedback and propose hypotheses worth investigating. Most teams end up doing both — the theory they want to test, and the patterns they didn't see coming.
Every interview, survey, and test you run keeps contributing.
Set up a study with method, participants, dates, and goal. Link facts as you process them. A finding from a usability test six months ago can still strengthen a hypothesis you're tracking today — without anyone having to remember to revisit it. Eight common research methods covered out of the box.
Collect it where it happens. Bring in what you already have. Add what came up in conversation.
Drop the embedded widget into your site or app and capture feedback at the moment users hit the issue. Pull in CSV exports from the tools you already use — Vyrdis recognises common column patterns and maps them automatically. Send feedback in from your own systems via API. Or add it by hand when something arrives over coffee. Every piece feeds the same body of evidence, regardless of how it got there.
Ask a question in plain language. Get back a structured answer, with the evidence it drew on.
Vyrdis searches through your facts, hypotheses, and conclusions to answer the question you actually asked. Findings come back as tables, statistics, lists, and callouts — with every source cited. Save the report, share it, or export to Markdown or HTML to drop into Confluence, Notion, or wherever your team writes.
Roles and organisations that match how teams actually work — and data that stays yours.
Four roles — Owner, Admin, Member, Viewer — cover most teams without bespoke setup. One login can move between organisations as your work does. Data sits in tenant-isolated storage, separated at the database level. And the things that matter most for trust — full data export, full data deletion, GDPR compliance — are self-serve, not a support ticket.
Free during early access. Drop in a CSV, or set up the widget — you'll see the chain build itself within the first few facts.