SignalForge is a production-style extraction system designed to transform high-volume, unstructured news articles into clean, validated, and analytics-ready structured data.
The system ingests article feeds, retrieves full article content, normalizes raw text, and applies schema-driven LLM extraction to produce deterministic JSON outputs capturing entities, events, statistics, quotes, and rankings. Rather than generating best-effort summaries, the pipeline enforces strict data contracts to ensure outputs are consistent, reliable, and suitable for downstream storage, analysis, and automation.
The project emphasizes reliability, validation, and reuse across domains; treating LLM extraction as a data production problem, not a text generation task.
This project demonstrates a schema-validated agent pipeline for converting messy, real-world article text into structured intelligence suitable for databases, dashboards, and monitoring systems.
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.