I’m Matthew, building Arbiter Briefs — an AI engine that helps founders make high-stakes decisions. This week we shipped financial PDF ingestion, and I want to walk through the architecture, the gotchas, and why we chose regex over ML for extraction.

The Problem

Our v1 was generating rulings based on web research + user input. But founders kept saying the same thing: “This would be way more useful if you actually read my financial data.”

So we added PDF upload. But now we had a new problem: how do you reliably extract structured financial metrics from PDFs that could be formatted a hundred different ways?

We could’ve gone full ML pipeline. Instead, we went pragmatic.

Architecture Overview

PDF Upload (multer)

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Storage (Railway volume)

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Parse (pdf-parse)

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Extract (regex + heuristics)

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Store (PostgreSQL JSONB)

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Use in Ruling (context injection)

Simple. Async.…