As conversational AI becomes embedded in research workflows, the challenge is no longer generating answers, but knowing when to trust them. This session introduces an accuracy-aware chat interface built into the Inspirient automated analysis platform and used by JAB Pet Services. By verifying every AI-generated statement against validated analytical outputs and visually flagging ungrounded claims, the system makes uncertainty visible and measurable.
Attendees will see how verification layers and human-in-the-loop design can turn AI from a black box into a transparent, accountable research assistant.
Key Takeaways:
1. Trust in AI-driven quant analysis must be engineered through verification, not assumed from fluent answers.
2. Making uncertainty visible and measuring how much of a response is grounded in validated outputs enables informed, critical use.
3. Accuracy-aware interfaces increase confidence and adoption while preserving methodological rigour and human judgement.
