Content engines
Content engines with review gates that stop bad pages
Ungrounded generation is fluent, plausible and unverifiable. That is exactly the failure mode that gets sites penalised, and no amount of prompt engineering fixes it. The problem is missing information, not phrasing.
- Grounded drafting
- Review gates
- Integrity checks
- Media pipelines
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.
Signs you need this
We build the production system around the model: retrieval against your real sources, structured templates, a named human with authority to reject, and automated checks on the claims that age badly. That combination is what separates scale that works from scale that gets you deindexed.
- Output capped by how fast humans can write
- AI-assisted volume went up, rankings went down
- Product claims nobody has verified in a year
- Prices and availability drift out of date silently
- Three agencies producing four visual languages
- Answer engines summarise competitors, not you
What you get
What is included
Grounded drafting
Retrieval over your catalogue, test results and live pricing. Generation becomes assembly, which is defensible.
Review gates
A named person accountable per section, with time and authority to reject. The gate only works if it sometimes stops something.
Integrity checks
Scheduled verification of price, availability and links. Retire rather than substitute. Quietly swapping a dead recommendation is how a review site stops being one.
Media pipelines
Image and video generation and editing wired into publishing, with explicit rules about what must be a real photograph.
Structured data
Schema that describes what a page actually is, with rating provenance gated on verifiable data rather than editorial opinion.
Link architecture
Internal linking and taxonomy that surfaces the pages that earn instead of burying them three clicks deep.
How it runs
How we deliver Content engines
- 01
Audit and ground
Find the real sources the engine will retrieve against. No sources, no engine.
- 02
Template and gate
Build the structures and the review queue, configured to your subject matter and risk tolerance.
- 03
Publish and measure
Ship at volume, measure against search and revenue per page.
- 04
Feed it back
What performs becomes the template. What doesn't gets retired.
Industries we build this for
Content engines questions
Something else? Email t@moher.ai and an engineer will answer it.
Will AI content hurt our rankings?
Unreviewed, undifferentiated content will. Content that's grounded, checked and structurally sound performs regardless of how it was drafted. The gates are the difference.
Do you publish unverified claims?
No. Anything that can't be confirmed is withheld rather than published with a caveat, and generated imagery is never presented as documentary photography.
Can you retrofit this onto an existing site?
Yes, and it's usually the fastest return available. Most publishers are earning without knowing which pages are doing it.
How does this help with AI answer engines?
Specificity, verifiable claims and clean structured data are what get cited. Vagueness gets skipped because a summariser has nothing to summarise.
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.