Extracting Data from Unstructured Text

Organizations are drowning in unstructured text: emails, support tickets, contracts, invoices, news articles, medical notes, and social media posts. Valuable structured data is buried in this text, but extracting it manually is slow, expensive, and error-prone. LLMs with structured outputs change this: they can read any text and populate a predefined schema with the relevant fields, at scale, with reasonable accuracy.

Information extraction (IE) is the process of automatically identifying and pulling structured facts from unstructured text. LLM-based IE dramatically outperforms earlier rule-based or classical NLP approaches because LLMs understand context, synonymy, and implicit information without needing hand-crafted regex patterns for every variation.

Information extraction powers many valuable business applications:

• Invoice processing: Extract vendor, line items, amounts, and due dates from PDF invoices for accounts payable automation
• Contract analysis: Extract parties, effective dates, payment terms, and termination clauses from legal documents
• Resume parsing: Extract skills, experience, education, and contact info from CVs for ATS systems
• Support ticket routing: Extract category, severity, affected product, and customer tier to route tickets automatically
• News monitoring: Extract entities, events, and sentiments from news articles for competitive intelligence