Define the study
Set the research goal, qualifying intake, and script segments so the interviewer knows when to talk, observe, and debrief.
How UserTold.ai works
Turn real interviews into verified work, then watch what happens after shipping.
UserTold.ai is one loop: design the study, interview users in context, extract source-backed evidence, verify review packets, route the right work, and keep completed fixes connected to future recurrence.
In-product interviewsEvidence packetsLinear completion sync
Operating loop
One participant session becomes a reviewable delivery decision
Interview signal
"I expected billing to live under account settings, not workspace settings."
/settings/billing00:42
Evidence packet
Billing settings findability issue, supported by source quotes and observed task behavior.
TypeStruggling moment
DecisionNeeds review
After shipping
Linked evidence resolves when Linear completes, then matching future evidence can resurface.
Product loop
From study design to recurrence review
The platform keeps every stage connected so interviews do not end as loose transcripts. Evidence remains tied to source context, delivery decisions, and post-release monitoring.
Run interviews in-product
Participants enter through your product. UserTold captures voice, transcript, screen context, and task behavior in the same session.
Extract evidence
Raw sessions become structured struggling moments, desired outcomes, and workarounds with quotes, confidence, and source context.
Review packets
Related evidence clusters into review packets that humans or agents verify before anything becomes delivery work.
Resolve and watch
When linked Linear work completes, current evidence resolves and future interviews are watched for possible recurrence.
Evidence before output
Review what happened before deciding what to ship
UserTold.ai treats evidence packets as triage bundles, not automatic orders. A project-aware human or agent verifies the source evidence before routing work.
Review packet
"I tried this flow three times and still cannot find where to change billing settings."
Each packet keeps the source quote, transcript or playback context, project goal, confidence, and delivery status visible for review.
Source quotePlayback contextConfidenceWork link
Source context
Quotes, transcript moments, page paths, and playback context stay attached so interpretation remains reviewable.
Conservative grouping
Packets summarize related evidence without pretending every cluster is automatically a delivery order.
Delivery handoff
Verified work can be pushed to GitHub or Linear with the evidence that explains why the issue exists.
Three surfaces
Operate the same loop from UI, widget, CLI, or MCP
The public website, dashboard, embeddable interviewer, and agent-facing tools all point at the same research-to-delivery workflow.
Dashboard
Review studies, interviews, evidence, packets, and routed work from the researcher workspace.
- Study setup
- Evidence review
- Packet triage
Widget
Embed interviews where the product behavior happens, with planned conversation and silent observation segments.
- Voice interviews
- Screen context
- Task debriefs
Agent tools
Use CLI, MCP, and REST surfaces when agents need JSON-first context for research and delivery workflows.
- CLI output
- MCP resources
- Tracker routing
Need implementation details? The docs cover study setup, the widget embed, CLI commands, and MCP integration.
Close the loop
Start with one in-product interview.
Define a study, capture a real session, inspect the evidence packet, and route only the work that survives review.