Gemini 2.5 Pro AI Chat Assistant

Research question: what blocks teams from using AI media examples in public pages? Evidence: storage ownership, rights review, and repeatable approval paths came up most often. Buyer constraint: teams need auditability before speed. Contradiction: they want faster output, but do not trust unmanaged links. Confidence: medium; five interviews show a pattern, not a market example. Next research: test whether reviewed template examples reduce maintenance work.

Signal: users may need clearer model selection help. Source: sales calls and rising model article traffic. Confidence: medium because sales notes are qualitative and traffic intent is broad. Why it matters: model confusion can slow first task completion. Next evidence: tag support tickets and prompt searches related to model choice.

Fit: Vendor A fits analytics needs better; Vendor B fits budget pressure. Risk: both need security review before purchase. Cost concern: Vendor A annual minimum may exceed current usage. Integration effort: Vendor B creates manual CSV work. Questions before purchase: security status, data export limits, and minimum term flexibility.

Theme: model choice clarity. Sample quote: users mention confusing model names. Frequency hint: three notes, likely worth validating. Product implication: add plain-language model guidance near the run panel. Follow-up question: does guidance improve first successful generation for new users. Edge case: team history request may belong to agency workflow research.