AI in media and publishing for the tagging, rights checks and repackaging your production desk does by hand
We build AI agents that draft metadata, check image and syndication rights, search your archive and prepare partner feeds inside the CMS and asset library you already run. An editor approves everything that goes out, and your archive stays in your own cloud.
Jason, our COO, runs the call and scopes the first workflow with you.
- Story filedFeature, 1,400 words, three agency photosDone
- Metadata draftedSection, people, places and topic tags from your taxonomyDone
- Rights checkedPhoto 2 is licensed for web only, not for syndicationFlagged
- Variants draftedNewsletter blurb, app alert and partner feed summaryDone
- Editor approvalTags, variants and a cleared replacement photo sent for reviewWaiting
- Sent to CMS and partner feedTags written back, feed item queued, run log entryAfter approval
What the agent can seeOnly the fields this task needs. Everything else stays in your systems.
- Story text and headline
- Visible
- Your taxonomy
- Visible
- Image licence terms
- Visible
- Contributor contracts
- Rights fields only
- Embargoed drafts
- Not retrieved
- Subscriber records
- Not retrieved
Illustrative run. Fields, taxonomy and rules are set per workflow during scoping. The agent never publishes or sends a feed item on its own.
Clients include
AI in publishing workflows, from filed copy to partner feed
Pick a queue with steady volume, written rules and a person who already checks the work before it goes out. Each of these six has all three. For workflows outside the newsroom, see 25 enterprise AI use cases by department.
Copy prep before the editor's pass
The agent checks a filed story against your style guide, flags banned terms and figures with no source in the copy, and drafts headline, standfirst and SEO fields. Your editor accepts, edits or rejects each suggestion.
How your style guide reaches the agentAutomated metadata tagging to your own taxonomy
The agent reads each story or asset and proposes sections, people, places, organizations and topics from the list you maintain, never a tag of its own invention. A producer confirms the set before it is written back.
How a context layer holds your taxonomyArchive search that cites the original story
A reporter asks a question in plain language and gets back the archive pieces that answer it, each with its date, byline and link, so they read the source instead of a summary of it.
When AI agents read your knowledge baseImage and syndication rights checks
Before a piece goes to a partner or a reprint, the agent matches every image and quoted excerpt against the licence terms in your rights records and flags anything not cleared for that use. Your rights desk decides each flag.
How we map your rules into the agent's contextSyndication and repackaging
One approved story becomes the newsletter blurb, the app alert and the partner feed item, each written to that channel's length and format rules. The editor approves the set before anything is sent.
See the content types Forge AI writesAd operations and revenue reporting
The agent pulls delivery from your ad server, checks each campaign against its insertion order and flags under-delivery while the flight can still be fixed. The database computes the figures, and the model writes only the summary.
How agents connect to your systemsHow a publishing AI build runs
The same team carries your workflow from the first call to the system running in production, and every build is tested in staging before it goes live. See how we work
Jason, COOAI Audit, if you need one
Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap. The fee is credited toward your first build.
- Jason, COO
Scope
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
- The archives and fields each agent may read
- The decisions that always go to an editor
- The timeline, before you sign
Maciej, CTOBuild
Built against your real content, in your environment, as production software from the first week.
- Staging first, on a copy of your CMS and archive
- Your editors use it on real stories before go-live
- Self-hosted n8n in your cloud
- Jason, COO
Integrate
Into production behind your auth, your governance and your monitoring, with your team trained on it.
- Separate development, pre-production and production environments
- Runs behind your own auth
- Your editors and producers trained on the system
- Maciej, CTO
Run and improve
Watched, fixed and extended in weekly sprints by the engineers who built it.
- Error alerts to the engineers who built it
- Weekly sprints that you direct
- The next workflow, scoped with you
What your production desk gets from a build with us
We build the agents around your house rules and run them in your environment.
| What you get | |
|---|---|
| You receive | Working agents inside your CMS, asset library and ad server |
| Where your archive lives | In your cloud, on self-hosted infrastructure |
| Fit to your rules | Built around your style guide, taxonomy and rights records |
| Decisions | Made by your editors, approved and logged |
| Your team | Editors and producers trained through our corporate AI training |
| Timeline | Scoped per workflow and agreed before you sign |
| After launch | The engineers who built it monitor and extend it |
What publishers ask about AI
Ask us something elseHow is AI used in media and publishing?
Mostly behind the page: transcription, copy prep, metadata tagging, archive search, rights checks, repackaging for other channels and ad reporting. Those back-end tasks are what 97% of the news leaders in the Reuters Institute's 2026 survey rated important. In the systems we build, an agent does the first pass and a person approves anything that reaches readers or partners. The same document pipeline behind our AI invoice automation can match ad network and agency invoices to their insertion orders.
Can AI tag our archive to our own taxonomy?
Yes, and it should use yours. The agent proposes tags only from the taxonomy you maintain, and anything it cannot place goes to a person instead of becoming a new tag. Older archive material is tagged in batches, with a producer sampling each batch before it is written back. The taxonomy, your style rules and your rights terms sit in the agent's context, which is what context engineering means in practice.
Will an agent publish without an editor?
No. In the systems we build, the agent drafts tags, variants and feed items and stops at the approval gate. An editor approves each one, and the run log records who approved what and when.
Does our archive or unpublished copy train anyone's model?
Training on your content is excluded in every build. The workflow runs on self-hosted n8n in your cloud, embargoed and unpublished drafts are left out unless a workflow needs them, and any external model call carries only the text that task needs, under your own agreement with that provider, or goes to a model you host.
Can the agent write in our house style?
It drafts against your style guide, banned terms and approved phrasing, loaded per title. A second agent checks each draft against the same rules and names each issue before your editor sees it. Your editors accept or reject its suggestions, and reporting and news judgment stay with your journalists.
Which CMS, asset library and ad server can you connect to?
We connect to any system that offers an API, a database view or a scheduled export. Forge AI already sends approved drafts to Webflow and WordPress, and other systems are connected per build. The AI Audit confirms what each of your systems allows before anything is quoted.
Is Ovidius a content agency?
No. We do not write articles for you or sell content by the piece. We build the pipeline your own editors run, deploy it in your environment and keep improving it after launch. Forge AI is the platform version, for teams that run many titles.
Where should a publisher start with AI?
With the queue that has the most volume and the clearest rules, usually tagging or syndication packaging. If you already know which one, book a discovery call and we will scope it. If you have several candidates, the AI Audit ranks them by value, effort and risk over 4 to 8 weeks and ends in a 90-day roadmap. It costs $10,000 to $100,000 depending on scope, and the fee is credited toward your first build. The same audit produces your enterprise AI strategy and AI roadmap. To prove one workflow on your own stories first, an AI pilot runs 2 to 6 weeks and costs $35,000 to $75,000, also credited toward the build.
Who maintains the pipeline once it is live?
The engineers who built it, in weekly sprints you direct, with error alerts going straight to them. If another team built workflows you already run, we take them over after a technical assessment of their quality. See AI managed services. If your producers want to edit workflows themselves, n8n training teaches them on your own pipelines.
Related industries
AI for insurance agents
Agents for agencies, brokers and MGAs that take quote intake, renewals, certificates and claims intake, with a licensed person approving coverage.
See AI for insuranceAI for SaaS companies
Agents that triage tickets, flag churn risk, prepare renewals and draft security questionnaire answers for your team to approve.
See AI for SaaSPut your first workflow on the board
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
