AI accelerates. The platform verifies. Researchers decide.
What the BI Agents do, what researchers own, and how every output is governed. Written for the insights team that will use the work, and for the procurement and security reviewers who will ask how it runs.
Three roles, and they do not overlap.
The private BI Agents work inside the same governed study record as the questionnaire, the field data, the tables, the advanced analyses, the transcripts, and the deliverables. They are scoped to one study. They are not a general assistant pointed at your data, and they are not a research team.
BI Agents accelerate
- Initial study and questionnaire builds
- Analysis support and pattern detection
- Transcript and open-end synthesis
- First-draft reports, dashboards, and toplines
The platform verifies
- Survey logic against the approved specification
- Bases, weighting, filters, and significance testing
- Numeric claims and source provenance
- Permissions, privacy, and audit history
Researchers decide
- Research design and methodological choices
- Which patterns are material
- How findings should be interpreted
- Caveats, recommendations, and final approval
Where AI is used in quant, qual, and UX.
The role is the same across the three, and the sign-off is always a named person. What changes is the kind of evidence being covered.
Quant
- Programs the survey from the approved document, with logic, loops, quotas, and terminates mapped
- Proposes NETs, banners, and custom tables against the house tabulation standards
- Reads across every question, table, segment, and open end to surface candidates
- Drafts the topline, the report, and the dashboard from verified study evidence
- The research lead sets the storyline, the implications, and the recommendations
Qual
- Drafts screeners, discussion guides, and in-session or homework exercises
- Transcribes every session with speaker attribution and review tools
- Drafts a first-pass topline shortly after each session
- Surfaces themes, tensions, and representative verbatims across sessions
- Every generated theme and takeaway must cite transcript evidence, and the moderator checks it against the conversation
UX
- Drafts moderator guides and screeners in the platform task and question formats
- Builds the screener logic, quotas, and terminations, then checks them back against the specification
- Transcribes and codes each session, timestamped against the task it belongs to
- Clusters recurring behavior into candidate themes across participants and tasks
- Assembles highlight reels from moments tagged live, for a researcher to trim and approve
What the BI Agent does not decide.
These are not settings. They are how the product is built, and they are the same on every study and every engagement model.
Never moderates a session
A person runs the room, in qual and in moderated UX alike.
Never assigns final severity
It can cluster and propose. A researcher decides what a finding is worth.
Never publishes without approval
Nothing reaches a stakeholder until the person whose name is on the work has approved it.
Never sets the method
Research design, sampling, weighting, and analytical approach are researcher decisions.
Never decides what is material
Pattern detection is not judgment. Which patterns matter is a research call.
Never writes the recommendation
The research lead writes it and stands behind it.
How a number earns its place in a deliverable.
The reason a draft can be trusted is not that the agent is careful. It is that the number is bound to its source, and a person signed the interpretation.
What the platform checks
- Every reported number keeps its question, base, weighting, and analytical context
- Significance testing is applied as the bases support it, not by default
- Survey logic is validated against the approved specification before launch
- Qualitative themes and takeaways must cite the transcript evidence behind them
- Expert findings are labeled as expert findings, never presented as participant data
What a researcher must approve
- The guide or screener, before any participant sees it
- The coding, checked against the recording
- What counts as a finding, and its severity
- The cross-session or cross-question story
- The caveats, the recommendations, and the final deliverable
Where your study data goes, and where it does not.
- Study content is processed through approved, server-side AI services under our approved configuration.
- It is not used to train provider models.
- Open-end text is processed by the provider only when an admin user explicitly invokes it, and is not stored by the provider.
- Agents are scoped to a single study record. There is no cross-client or cross-study pooling.
- AI interactions are logged for review, alongside the platform's sign-in and access audit trail.
- No respondent data is shared with advertising or analytics services.
The full posture, including encryption, access control, retention and deletion, subprocessors, and the InfoSec questionnaire, is on the security and compliance page.
What changes by engagement model, and what does not.
The governance does not move. What moves is whose researchers are doing the deciding.
Your team runs it
Your researchers use the agents and own every approval point. Our specialists are available for advanced designs.
Your team leads, ours assists
Your researchers own the study. BEYOND specialists support selected stages, and the approval points stay with the named owner on each side.
Our team runs it
BEYOND researchers design the study, run it, and deliver the findings. A BEYOND research lead is the named approver.
Built by researchers. AI designed into the workflow from day one.