Show HN: Aqqai – Every answer, checked before you see it

Aqqai launched a demo on Hacker News that fact-checks AI-generated answers in real time before displaying them to users. The project, posted by creator Vineetyadav2, aims to address one of the core problems with large language models: hallucinations and false information presented with confidence.

The demo, accessible at demo.aqqai.in, implements a verification layer between the AI's response generation and user visibility. Rather than returning answers directly from a language model, the system appears to validate factual claims before surfacing them. The exact mechanism—whether it uses external APIs, knowledge bases, or another verification method—is not detailed in the public launch, though the core value proposition is immediate: users get checked answers, not raw model output.

The launch appeared on Hacker News as a Show HN post, a category for sharing projects and launches directly with the community. The post attracted limited initial engagement, with 1 point and 1 comment as of the launch date, suggesting early-stage visibility rather than widespread adoption.

The broader context reflects growing concern in AI development about response reliability. As LLMs have become more accessible and integrated into production systems, fact-checking and hallucination mitigation have emerged as necessary layers, not optional features. Aqqai positions itself as a pragmatic tool for teams building chatbots, search interfaces, or question-answering systems where accuracy matters before user-facing responses go live. The demo stage suggests the team is still validating the approach and gathering feedback before a wider release or integration into other applications.