Processing thousands of news sources in real time requires careful architectural planning. At JOLPA, we've built a robust syndication pipeline that ingests, normalises, and enriches news content for downstream AI applications.
Ingestion layer
We use a combination of RSS/Atom feeds, APIs, and web scraping (where permitted) to collect content from over 10,000 sources. A distributed crawler respects robots.txt and implements exponential backoff to avoid overwhelming origin servers.
Normalisation and deduplication
Raw content varies wildly in structure. Our normalisation layer extracts clean text, metadata, and publication dates, storing everything in a consistent schema. Near‑duplicate detection prevents the same story from appearing multiple times.
Enrichment with synthetic metadata
Each article is passed through a suite of NLP models that generate topic tags, sentiment scores, and named entity recognition. This metadata makes the feed immediately usable for training and inference.
Delivery
Clients can consume the feed via streaming API, daily dumps, or direct database access. We handle the infrastructure so data scientists can focus on model development.
Interested in a custom news feed? Contact JOLPA to discuss your requirements.