@mdancho84: RIP document extractors. Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools. Here’…

RIP document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does:

What it does:

→ Extracts structured data from messy text → Grounds every field to the exact source location → Handles 100+ page docs → Generates interactive HTML for verification → Works with Gemini + local models

What it replaces:

→ Regex/fragile parsing → Custom NER pipelines → Expensive extraction APIs → Manual data entry

Here’s the career takeaway:

This is the shift from “task-based data work” → workflow ownership.

Anyone can “extract text” now.

What companies pay $200k/yr for in 2026 is:

Turning that extraction into an AI/DS workflow that ships decisions (saves time / reduces costs).

Real business use cases for doc extraction:

→ Invoice / PO processing → Customer feedback → themes → actions → Support tickets → root causes → SOP updates → Legal docs → clauses → risk summary → Financial reports → KPIs → exec brief

Want to learn how to build + ship AI and Data Science projects (that businesses actually want in 2026)?

On July 14th, I am hosting a free workshop to help you get started with AI + DS projects in Python.

Register here (500 seats): https://learn.business-science.io/ai-register