PlatformInfrastructure
Attribution spine
First-party click tracking, offer routing, s2s postbacks. Built once, inherited by every surface.
Media & publishing
AI development for media and publishing
Publishers have the most valuable asset in AI, a large archive of edited text, and the most to lose from careless use of it. Ungrounded generation damages trust and search performance, and archives that are not searchable earn nothing. We build systems that make the archive useful and keep editors in control of what is published.
We build answer engines and search over your archive, editorial copilots for research, summaries and headlines with fact checks, tagging and metadata pipelines, and grounded content workflows with mandatory human review.
Talk to an engineer →Scope in one week. You own the repo from commit one.
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.
Problems we solve in media & publishing
- Decades of archive content are effectively unsearchable.
- Tagging and metadata are inconsistent, so related content never connects.
- AI-assisted drafts reach editors with unverified claims.
- Readers want answers and get a list of ten links instead.
- Nobody can say which content earns revenue and which costs money to host.
- Licensing questions about AI use of the archive have no technical answer.
What we build
Systems we build for media & publishing
01
Archive answer engine
Reader-facing answers over your archive with citations to the original articles, measured for accuracy and attribution.
02
Editorial copilot
Research, summaries, headline options and related-coverage lookup for editors, with every claim linked to a source.
03
Fact and claim checks
Automated checks of names, figures and quotes against sources before a draft reaches an editor.
04
Tagging and metadata pipeline
Entity, topic and taxonomy tagging across the archive and new content, with editor review on low-confidence tags.
05
Grounded content workflow
Drafting from approved sources only, structured templates and a named human gate with authority to reject.
06
Archive data layer
Content, metadata and performance data in one warehouse so editorial and commercial decisions use the same numbers.
Shipped in this industry
Running in production
Client names stay under NDA. The shapes do not.
Media & publishing questions
Something else? Email t@moher.ai and an engineer will answer it.
Will AI-assisted content hurt our search performance?
Ungrounded, unreviewed content will. Grounded drafts with fact checks and an editor gate perform like the rest of your journalism. The review gate is the difference.
How do you handle copyright and archive licensing?
The system runs in your cloud on your content. Model providers are used under terms with training disabled, and third-party wire or licensed content is excluded or flagged according to your license terms. We work inside your legal review.
Can readers trust answers generated from the archive?
Answers cite the articles they come from and decline when the archive does not cover the question. Accuracy and citation rates are measured in the eval suite on every release.
Do you work across a portfolio of titles?
Yes. A shared retrieval and tagging layer across titles is a common shape for this work.
What does a first engagement look like?
Usually the tagging pipeline or archive search, since both make later features possible. Six to ten weeks to production.
Tell us what you need built
Four quick questions and an email. An engineer reads every brief and replies within one business day with next steps, and a written scope follows within a week.
- Written scope and price within a week
- Production deploy by week three
- Your repo and cloud from the first commit
- Senior engineers only, no account managers
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.