TxDesk

The research on how AI systems decide what to trust is clearer than you might think. It comes down to a few core signals that are measurable and actionable.

Citations are the strongest signal. A study from the University of Notre Dame and Deloitte found that simply having citations in an AI response increases user trust significantly even when the citations themselves are random . The presence of sources signals credibility. The act of checking them signals distrust.

E-E-A-T is no longer just Google's framework. It has become the core principle AI systems use to decide what (and who) to trust . AI systems prioritize Experience, Expertise, Authoritativeness, and Trustworthiness when evaluating sources. They look for signs that a real practitioner stands behind the advice detailed bylines, credentials, first-hand narratives, and verifiable experience

Perceived gatekeeping and information completeness matter. Users trust Google because it performs credible gatekeeping . Wikipedia earns trust through collective curation. AI systems look for similar signals: is there evidence of editorial oversight? Is the information comprehensive enough to answer the query completely?

Hey Product Hunt, I am the solo founder behind TxDesk.

I built it around one idea: in crypto, a confident wrong answer costs you your wallet. A general AI model answers from training data with no live view of the chain, so it autocompletes something fluent and possibly wrong. When the cost of being wrong is your money, the model cannot be the source of truth. The chain has to be.

I started building LineageLens because I kept asking a question that nobody on my team could answer: if a CVE gets traced back to an AI-generated line in production next week, what evidence do we actually have?

Not "we use Copilot and review everything." Machine-readable evidence: which model generated which lines, with what prompt, at what confidence, and whether a human reviewed the AI context before it shipped.

For most teams, the answer is nothing. The Cloud Security Alliance put a number on it this month: 81% of organizations have no complete visibility into where AI-generated code lives in production. That gap grows larger every sprint.

LineageLens intercepts AI tool traffic at the proxy layer to capture provenance records at generation time -- the only moment when model, prompt, file path, and edit context all coexist. Then the blame engine maps those records onto current file contents: