ParaAIS
An experimental AI interaction framework built around a strict principle: the assistant should never sound more confident than its evidence allows.
Many AI “failures” are presentation failures: the model guesses, and the UX frames it like truth. ParaAIS explores how to build assistants that earn trust by being explicit about boundaries, sources, and uncertainty.
If evidence is missing: redirect to verified material, offer next-best help, and avoid inventing specifics.
- Tiered claim confidence (confirmed / inferred / unsupported)
- Evidence-aware responses and safe redirection
- Refusals that remain helpful (no dead ends)
- Clear separation between retrieved knowledge and model reasoning
- Recruiter-facing portfolio assistants
- Internal knowledge base chat UX
- High-trust professional tools
- Governance-aware GenAI demos
ParaAIS is an applied exploration of “trust by limitation.” It prioritizes safe interaction patterns over persuasion, and frames uncertainty as a feature — not an embarrassment.
The win condition isn’t “sounds smart.” It’s “remains reliable under pressure.” (Basically: no confident improv in a job interview. We leave that to LinkedIn.)