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A product-feedback intelligence prototype for turning qualitative responses into structured themes and decisions.
Verdict AI was built to help product teams move from scattered qualitative feedback to structured, actionable insight. The prototype focuses on extracting signal, grouping related feedback and presenting direct recommendations. Its current value is the product framing and working analysis flow; it does not claim validated research quality, team collaboration, billing or production-scale ingestion.
Translating ambiguous multi-stakeholder requirements into deterministic, verifiable software under strict latency, cost, and usability boundaries.
Use structured LLM output to keep analysis predictable for the interface.
Design the experience around decisions and next steps rather than generic sentiment charts.
State non-goals clearly so prototype scope is not confused with a production research platform.
Keep future evaluation requirements explicit: evidence coverage, theme precision, duplicate-theme rate and human correction.
Test the RAG retrieval pipeline by asking specific questions about this case study: