SafisAI started with a practical concern: when a page uses AI, what can a normal user actually verify? That question led to a Chrome extension focused on local scans, careful wording, and a more honest way to surface AI-related signals.
We are not trying to sensationalize AI use. We want users to see meaningful signals, understand the evidence behind them, and decide for themselves what to trust.
People should not have to guess whether they are interacting with AI, reading AI-generated material, or sharing information into an AI workflow. As AI becomes part of everyday software, disclosure should be treated as basic product hygiene, not an optional extra.
If AI is active in a product experience, users should be able to tell clearly and quickly. It should not be hidden behind vague wording, confusing UI, or buried documentation.
AI-generated text, images, and outputs should carry clearer disclosure norms so people can understand what they are seeing and make better judgments about trust, authorship, and provenance.
Companies should be able to explain what an AI system is doing, what signals support that claim, and what user impact follows from it. Transparency should create accountability, not just optics.
We avoid strong claims unless we have strong evidence. SafisAI is designed to separate what was observed directly from what was disclosed by the site and what remains possible.
Detection is meant to happen in the browser, not through a remote SafisAI service. The product direction stays grounded in on-device analysis and minimal data movement.
SafisAI should be serious, clear, and trustworthy. The product should help people understand what was found without turning every signal into a worst-case interpretation.
The goal is to help users make informed choices, not to decide for them. We show the signals, explain the evidence, and leave the judgment to the user.
The more AI becomes embedded into search, writing, customer support, images, assistants, and everyday workflows, the easier it becomes for users to lose track of what is happening around them. That is exactly why clarity matters now.
Users trust products more when they can see what is happening instead of being asked to assume good intent.
Meaningful disclosure helps people understand when they are entering AI-driven systems and when their actions may affect AI outputs, records, or downstream decisions.
Better transparency creates clearer expectations for builders, platforms, and institutions using AI in public-facing products.
We want a future where AI use is easier to identify, AI-generated outputs are easier to disclose, and the systems behind those outputs are easier to question. SafisAI starts in the browser, but the goal is broader than one extension.