Trust in AI is built on transparency, not just accuracy scores. In our extraction pipelines, we treat every data point as a claim that must be proven. By implementing a system that requires a citation for every one of the several dozens fields we track per topic, we transform a probabilistic AI output into a deterministic, auditable record. This eliminates the "black box" problem and gives users the ability to jump directly to the source of any data point.
In the discussion around Large Language Models, the fear of hallucinations — incorrect information — often dominates the conversation. Achieving 100% precision is a prerequisite for any financial data system; "no garbage in" is a non-negotiable rule. However, for professional-grade extraction with LLMs, the more difficult challenge is recall. If an LLM encounters a complex website structure or a 50-page legal document, it often loses focus, missing critical details buried 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 remarkably reliable database through a rigorous, labor-intensive process where mid-level staff reviewed every entry made by junior analysts. However, even the most meticulous manual process eventually hits a wall: the velocity of information. In the space of e.g. investments, data ages rapidly, and a manual team simply cannot scale their output to keep pace with the market without a linear — and often unsustainable — increase in headcount.