PROPOSED AI APPLICATION

Research interview theme explorer

Qualitative evidence is difficult to organize

Product research and design departmentsHistorical data model pilotProposed solution scope

The problem and the proposed outcome

Application: Product research and design departments — Research interview theme explorer
Problem to solve: Qualitative evidence is difficult to organize
Target buyer: Research leads, product design teams
Intended use: Employee / expert own workflow; internal business solution
Benefit to test: Test whether evidence organization assists research without hiding minority or contradictory observations.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Unsupervised theme exploration
Method code: K
AI contribution: Use embeddings and clustering to propose themes in permitted evidence; reviewers name, revise and validate the themes.
Output artifact: Candidate themes with representative evidence and reviewer labels.
Core modules: Authorized evidence → embedding / clustering → theme proposals → researcher validation
Simple baseline: Manual thematic coding and keyword grouping.
Why test AI: Could organize diverse qualitative evidence; clusters are not automatically valid findings.

Pilot track: Historical data model pilot
Required data: Consented transcripts; reviewer theme examples
Data readiness: A representative permitted corpus, source traceability and qualified theme-review criteria are needed.
First development step: Sample permitted evidence, establish a manual theme baseline and document researcher review criteria.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Research leads, product design teams: show a recent case where qualitative evidence is difficult to organize, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Researcher-rated theme usefulness
Model evaluation: Assess reviewer-rated coherence, source coverage, stability and theme usefulness against manual coding.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: No special flag; domain review still applies
Human review boundary: Reviewer or user confirms labels, exceptions, links or shortlist relevance; no automatic adverse or consequential decision.
Domain review: Confirm data ownership, access, source rights, reviewer authority and correction routes with the operator.
Related micro-ERP catalogue IDs: IWA-B-DE6C381E864D; IWA-B-EF53CF5BDCA9; IWA-B-5183B1E6E908
Integration status: Potential segment link only; no product integration implemented.