Trust in AI is built on transparency. Every extracted field carries a citation back to the exact source passage, and the full extraction set is scored against a benchmark. They answer different questions, at different moments. The benchmark answers whether the pipeline is good enough to ship; that is my question, once, before release. The citation answers where a specific number came from; that is the user's question, every day after.
Most of the worry around large language models lands on hallucinations, meaning incorrect information. Achieving 100% precision is a prerequisite for any financial data system. According to my evaluations for professional-grade extraction with LLMs, the harder challenge is recall. If an LLM encounters a complex website structure or a 50-page legal document, it misses details buried deep in the text.
High-quality data is often a point of pride for any company collecting them. In a recent engagement, the client had built a reliable database through a rigorous, labor-intensive process where mid-level staff reviewed every entry made by junior analysts. Even the most careful manual process eventually hits a wall: the velocity of information. In investments, data ages rapidly, and a manual team cannot scale their output to keep pace with the market without a linear increase in headcount.