# Building DisconfirmAI: An AI investment research workspace that looks for counter-evidence first > Source: > Published: 2026-10-27 01:22:06+00:00 When you use a general-purpose LLM to analyze a stock or read an earnings report, it tends to agree with whatever premise you put in the prompt. If you ask why a company looks undervalued, it will assemble a convincing bull case. In financial research, that habit is risky. I built [DisconfirmAI](https://disconfirmai.com/en) around a simple rule from Karl Popper and value investing: before building conviction in a thesis, look for the specific evidence that would prove it wrong. Here is how the workspace is structured: The [Daily Reading](https://disconfirmai.com/en/reading) feed curates AI industry and market developments from primary sources, keeping original links and publication dates. Editorial annotations use three fixed labels: In the [Research](https://disconfirmai.com/en/features/research) workflow, questions about earnings releases, multi-year financials, and SEC 13F institutional holdings (calculated strictly by share-count changes rather than market-value fluctuations) are split into three sections: **Verified**, **Inferred**, and **Doubtful**. It does not output buy/sell recommendations or price targets. In [Investment Framework](https://disconfirmai.com/en/features/system), you write down your circle of competence, entry rules, and the exact conditions that would force you to revise or exit a position. [Radar](https://disconfirmai.com/en/features/radar) scans those conditions on a schedule. When nothing material changes, it stays quiet and logs `scanned-and-empty` in the audit trail. Everything below is open in the browser without registration: If you analyze tech or AI companies or build financial research workflows, I would love your feedback on the tools and the falsification approach: [https://disconfirmai.com/en](https://disconfirmai.com/en)