Eval directory

Evals for Listen Labs

Eval coverage for Listen Labs, mapped from its public product surface.

About Listen Labs

Listen Labs runs AI-moderated qualitative research interviews with real human participants and turns the responses into structured insights for brand, product, and UX teams. It covers use cases such as concept and prototype testing, usability testing, creative testing, brand perception, and consumer journey mapping. The platform includes participant recruitment, context-aware follow-up questions, and screen sharing, with recent additions like MaxDiff, Visual Insights, Research Library, and a Research Agent.

Industry

AI-driven qualitative customer research platform

Headquarters

San Francisco, CA

Website

listenlabs.ai

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We'll build out the full library — runnable test cases with inputs, expected behavior, and pass/fail checks — in your Corsac workspace.

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Coverage map

What would you measure for Listen Labs?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Study Design & Question Generation

Turning a stated research objective into a fielded study: discussion guide, stimuli handling, and the right method for the question being asked across the use cases Listen Labs names.

“Listen Labs provides AI-driven qualitative research services.” listenlabs.ai

Mapped capabilities

4 capabilities

Objective-to-guide translation
Converts a business question into a non-leading interview guide scoped to the stated use case.
Method selection
Chooses among concept/prototype testing, usability, creative testing, brand perception, journey mapping, or MaxDiff for a given objective.
MaxDiff setup
Item lists, ranked-choice framing, and design constraints for trade-off exercises.
Stimulus and prototype handling
Presenting mockups, creative variants, or price points in a consistent order and framing across participants.

AI-Moderated Interviewing

The live conversation with a real human participant: probing depth, neutrality, and session mechanics including screen sharing.

“What people think, why they think it, and what to do about it. From real, human interviews.” listenlabs.ai

Mapped capabilities

4 capabilities

Context-aware follow-ups
Probes a vague or thin response using the participant's own words without supplying the answer.
Moderator neutrality
Avoids leading, loaded, or double-barreled questions and does not endorse a concept mid-interview.
Screen-sharing sessions
Directing and observing a participant walking through a product or competitor comparison.
Session pacing and scope control
Covering the guide within budget, and handling off-topic, refused, or unusable answers.

Illustrative example

Input: Usability study on a checkout flow. Asked what they thought of the payment step, the participant says: "It was fine, I guess." Produce the next moderator turn.
Expected behavior: Asks a single open follow-up that reuses the participant's own wording and points back to the payment step. It does not offer candidate reasons, does not stack a second question, and does not imply a preferred answer.

Participant Recruitment & Sample Quality

Reaching the intended people and keeping the sample trustworthy, across the platform's own panel and customer-supplied lists.

Mapped capabilities

4 capabilities

Screener construction
Filters by demographics, behavior, and job title that match the stated target without telegraphing the qualifying answer.
Bring-your-own-participants
Fielding a study against a customer's own customer list rather than the panel.
Fraud and inattentive-respondent detection
Flagging responses that indicate a misrepresented or non-genuine participant, a stated concern in B2B samples.
Sample composition reporting
Stating who was actually reached and where the realized sample diverges from the requested quota.

Insight Synthesis & Reporting

Converting transcripts into the structured artifacts the marketing surface shows: preference splits, verbatim quotes, emotional signals, hesitation moments, and say/do gaps.

Mapped capabilities

4 capabilities

Quote grounding and attribution
Every verbatim traces to a real transcript, participant, and question.
Quantified outputs
Preference percentages, ranked-choice orders, and MaxDiff scores consistent with the underlying responses.
Behavioral and emotional signals
Say/do gaps, hesitation moments, and emotion breakdowns reported with their evidentiary basis.
Visual Insights outputs
Chart and summary artifacts that match the numbers they are drawn from.

Illustrative example

Input: Twelve interview transcripts from a brand perception study. Produce a one-paragraph summary of how participants describe the brand, supported by three verbatim quotes.
Expected behavior: Each of the three quotes appears word-for-word in one of the supplied transcripts and carries a participant identifier and the question it answered. Claims the transcripts do not support are omitted rather than smoothed into the summary.

Research Agent & Research Library

The agentic and knowledge-reuse surface: an agent that carries research work forward, and a library of prior studies to draw on.

Mapped capabilities

4 capabilities

Agent task scoping
Interpreting an open research request and proposing a concrete study or analysis rather than acting beyond the ask.
Prior-study retrieval
Surfacing relevant past research from the Research Library instead of re-fielding a settled question.
Cross-study synthesis
Combining findings across studies while preserving each study's sample and date context.
Escalation to a human researcher
Declining to answer from insufficient evidence and stating what would need to be fielded.

Participant Data & Privacy Boundaries

The data-handling surface described in the published policies, including the explicit split between participant data and website/customer data.

Mapped capabilities

4 capabilities

Participant vs. customer data separation
Applying the study privacy policy to participants and the website policy to customers and visitors.
PII in transcripts and outputs
Handling identifying details a participant volunteers before they reach a client-facing report.
Rights, retention, and transfer requests
Responding to access, deletion, and cross-border storage questions consistent with the published policy.
Underage and ineligible participants
Recognizing a participant who should not be enrolled and stopping rather than continuing the session.

Coverage is mapped from Listen Labs's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Listen Labs test?+

The coverage map is generated from Listen Labs's own public product surface (AI-driven qualitative customer research platform): 6 scoring areas — Study Design & Question Generation, AI-Moderated Interviewing, and Participant Recruitment & Sample Quality, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Listen Labs evals scored?+

Every case generated for Listen Labs — across Study Design & Question Generation and AI-Moderated Interviewing and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Listen Labs library include?+

The full Listen Labs library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Objective-to-guide translation and Method selection under Study Design & Question Generation); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Listen Labs or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Listen Labs areas and set them up in a Corsac workspace, where you can run every test case against Listen Labs or your own agent with your own data.