PUB.01
Newsroom Agents
Research, transcription and checking agents for journalists that answer only from cited sources and write to unpublished CMS drafts; an editor signs off everything published.
Open pageIndustry
Newsroom AI,
with an editor on the byline.
An editor signs off every story. Research agents for journalists, search across decades of archive, and the rights layer behind AI content licensing. The model does the legwork; an editor stays answerable for what is published.
Last reviewed 30 October 2026 · Engineering guidance, not legal or regulatory advice.
In context
AI in journalism and publishing means using language models for the work around the story: research, transcription, tagging, archive retrieval and rights management, while a named editor stays accountable for every published word.
Where it breaks today
01
A wrong fact costs the masthead
A publisher’s asset is trust, and a confidently invented quote or date spends it in public. Generic writing tools optimise for fluent copy, which is exactly the wrong property when the question is whether the copy is true.
02
The archive cannot be found
Decades of reporting, photography and broadcast sit in a CMS and a DAM under inconsistent tags, so reporters re-research stories the title already covered and the back catalogue earns nothing.
03
Your content is training someone else’s model
AI crawlers read the site whether or not you have said they may. Without a clear rights inventory and machine-readable reservations, there is nothing to license and nothing to enforce.
OUR APPROACH
The legwork goes to the model. The judgement does not.
Most of what AI in publishing is good at is unglamorous: transcribing an interview, pulling every earlier story on a subject, tagging a photo set to IPTC fields, summarising a court filing with a citation to each page. We build for that layer, and every output carries its source so a journalist can check it in seconds rather than trust it.
What we do not build is a system that publishes on its own. Under Article 50 of the EU AI Act, AI-generated text published to inform the public must be disclosed unless it has been through human review and someone holds editorial responsibility for it. We design the workflow so that review is real and recorded. How you meet the obligation, and your own disclosure policy, are decisions for your editors and counsel.
Rights run the other way. The EU copyright directive (2019/790) lets you reserve text-and-data mining in machine-readable form, and the press publishers’ right in Article 15 is the basis many licensing negotiations start from. We build the inventory and the tooling; the commercial terms stay yours.
HOW IT DEPLOYS
PHASE 01
Map the desk
We sit with the newsroom and the archive team to find the research, tagging and retrieval steps that eat the most hours, and the ones where an error would be published.
PHASE 02
Connect the CMS and DAM
The system reads from the tools you already run, such as WordPress VIP, Arc XP or Naviga for copy and your DAM for media, and writes suggestions back as drafts, never live.
PHASE 03
Sign-off stays editorial
Every suggestion carries its sources and a log of who accepted it, so the editorial record shows a person made each call.
What we build
01
Newsroom Agents
Research, transcription and background agents for reporters, each answer cited to a source document, a transcript timestamp or an earlier story. Drafting assistance stays inside the CMS as a draft.
SEE NEWSROOM AGENTS →02
Archive Search and Tagging
Semantic search across text, photo and video archives, with AI content tagging mapped to your taxonomy and IPTC standards, so the back catalogue becomes something reporters and readers can use.
SEE ARCHIVE SEARCH →03
Content Licensing Infrastructure
A rights inventory of what you own and may license, machine-readable opt-outs, crawler monitoring, and clean licensed datasets delivered with usage tracking.
SEE CONTENT LICENSING →04
Retrieval Over Your Own Reporting
The architecture under all three: retrieval-augmented generation over your own corpus, so answers come from what your title published rather than what a general model remembers.
SEE RAG SYSTEMS →From the other side of the inbox: QQuantum.AI also runs its own Autonomous Press Desk, an outreach agent that researches journalists whose beat matches a story and sends each an individual pitch. The journalist decides whether to cover it.
Controls we implement
SOURCE CITED ON EVERY ANSWER
DRAFTS NEVER PUBLISH THEMSELVES
EDITOR SIGN-OFF LOGGED
NO TRAINING ON YOUR ARCHIVE
TDM RESERVATIONS MACHINE-READABLE
SOURCE PROTECTION BY ROLE
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security certifications, and your editorial standards and legal obligations remain yours — see our trust centre.
Media & publishing use cases
-
-
PUB.02
Archive Search
AI content tagging and semantic search for text, photo and video archives, mapped to house taxonomies and IPTC fields, with low-confidence tags held for an archivist.
Open page -
PUB.03
Content Licensing
Rights inventory, machine-readable TDM reservations, AI crawler monitoring and licensed dataset delivery. Nothing is offered until its ownership is classified.
Open page
Questions
Questions about Media & Publishing.
How is AI used in journalism?
Mostly behind the story rather than in it. Newsrooms use AI to transcribe interviews, search their own archives, summarise long documents with page citations, tag photos and video, translate, and draft routine formats such as results tables. The reporting, the judgement about what is true and newsworthy, and the decision to publish stay with journalists and editors. The systems we build follow that split and record who accepted each suggestion.
Can AI write news articles?
It can produce fluent text, which is not the same thing. A model has no way of knowing whether a quote was said or a figure is current unless it is grounded in a source you trust, and even then it can misread it. We build drafting help that only works from supplied, cited material and lands in the CMS as a draft. An editor reads it against the sources before anything is published.
Do publishers have to disclose AI-generated content?
In the EU, Article 50 of the AI Act requires disclosure when AI-generated or manipulated text is published to inform the public on matters of public interest, unless the content has had human review or editorial control and a person holds editorial responsibility. Deepfake images and video carry their own disclosure duty. Many publishers go further in their own policy. What applies to you is a decision for your editors and legal advisers.
How should publishers handle AI companies training on their content?
Start with an inventory: what you own outright, what is syndicated or contributor content you cannot license, and what is already being crawled. Then make your reservation machine-readable, through robots.txt rules per crawler and a text-and-data-mining reservation under the EU copyright directive. That gives you a position from which to negotiate or decline licences. We build the tooling; the commercial and legal strategy is yours.
What are the ethical risks of AI in journalism?
Invented facts and quotes, undisclosed synthetic content, bias inherited from training data, and leaking confidential sources into third-party systems are the main ones. Architecture addresses some of it: grounding answers in cited sources, keeping source material on infrastructure you control, and restricting access by role. The rest is editorial policy and training, which no system replaces.
