Assistants describe Nortium without pricing or plans; competitors expose structured pricing the model can cite.
nortium.com/pricing · no structured data
Growth engineers
AI visibility that behaves like code, not a dashboard.
CitePatch treats each fix as a reviewable diff and each deploy as a controlled experiment. Patches open where your content already lives — a pull request, a CMS draft — and every result is traceable end to end.
Observed
Assistants describe Nortium without pricing or plans; competitors expose structured pricing the model can cite.
InferredConfidence: 4/5
The evidence suggests missing structured data, not missing content.
Proposed
Add Product/Offer JSON-LD to the pricing route.
Effort: SRisk: low
app/pricing/structured-data.tsx
"@type": "Product","name": "Nortium""name": "Nortium", "offers": {"@type":"Offer","price":"49"}The difference
What changes when you run the loop
Without CitePatch
- AI-visibility work lives outside your stack, in dashboards you can't diff.
- Suggestions are copy-paste, with no review or rollback.
- No control group, so real wins and noise look identical.
- Nothing is versioned — you can't audit what changed or why.
With CitePatch
- Fixes open as pull requests in the repo that owns the page.
- Every change is reviewed, merged and revertible like any code.
- A held-out control makes each result attributable.
- Prompt → answer → citation → page, auditable end to end.
The loop, as a pipeline
Observe, patch, and measure — without leaving your stack.
No copy-paste suggestions and no black box: the same discipline you'd expect from a well-run experiment.
- 01DiscoverFind the buying questions worth tracking.
- 02ObserveSee how AI answers them, verbatim.
- 03DiagnoseTrace each gap to the evidence behind it.
- 04PatchDraft the smallest fix, grounded in your facts.
- 05ApproveA human reviews and signs off.
- 06DeployShip it where your content lives.
- 07VerifyRe-observe the result against a control.
- 08LearnKeep what worked; queue the next gap.
- ↩ the loop is deliberate
Division of labour
You own the repo. CitePatch proposes the diff.
What stays yours
- The repository, review process, and deploy pipeline
- Merging, reverting, and the final say on every change
- Where CitePatch can and can't open changes
What CitePatch runs
- Grounding every patch in your approved facts — nothing invented
- Opening the smallest change as a pull request or CMS draft
- Running a fair before/after against an untouched control
- Exports and an API so it fits your existing workflow
What's in the record
Auditable from prompt to deploy.
Every artefact carries its epistemic state, so a result never claims more than the data supports.
- Observed
Raw per-surface answers and citations, captured verbatim.
- Inferred
The prompt-to-page chain behind each gap.
- Proposed
A grounded diff, ready to review in your own tools.
- Verified
A controlled before/after with a stated verdict.
One fact, all the way to a patch
"@type": "Product","name": "Nortium""name": "Nortium", "offers": {"@type":"Offer","price":"49"}Another role on your team?
For technical teams
Frequently asked questions
How do patches reach my codebase?
As a pull request in the repo that owns the page, or a draft in your CMS. You review the diff, then merge and deploy on your own terms.
Can the model invent facts in a patch?
No. Every patch is grounded in your approved Brand Truth. If a fact isn't approved, it can't ship in a change.
Is there an API and exports?
Yes. Observations, evidence, and experiment results are available via exports and an API so you can wire them into your existing reporting.
Put AI visibility under version control.
Start with a Free Audit, or book a demo to see a patch open as a pull request on your own content.