Extracting Structured Data from Documents with an LLM and a Self-QA Step
Book a call with MatteoThe problem
Getting an LLM to pull numbers out of a PDF or press release is easy in a demo. Doing it for thousands of documents a day is not: the model grabs last year's figure instead of this year's, confuses net result with operating profit, invents a value when the field is missing - and you have no reliable way to know which outputs to trust. Adding more instructions to the prompt makes it slower and not much better, and stakeholders want an accuracy number you can't give them.
The solution
A walkthrough of the pipeline I run product on for financial disclosures: a configurable taxonomy to find candidates, a focused extraction prompt per datapoint, then an LLM QA pass that returns OK/KO with a reason - plus how we measure quality and latency. Client data stays private; I show the design. Or bring your extraction use case and we review prompts, schema and evaluation live.
How the session works
Live demo & walkthrough
The expert opens their real project and shows how it works: the setup, the decisions behind it and the pitfalls they hit along the way.
Setup review & troubleshooting
You show your screen or project. The expert walks through it with you, points out what to improve and gives concrete recommendations.
Which format fits depends on your focus - you pick it when you book.