AEO for in-house marketing teams
An engineering team for your marketing team.
AI search is won with schema, crawler access, log analysis, and repeatable measurement. That is engineering work, and it is sitting behind your product roadmap.
Canonry does that part. No new headcount, no tool to learn, and no ticket waiting on a sprint. Start with the free audit for a dated baseline.
Reporting up
A screenshot is not a number.
AI answers shift by account, geography, history, model, and time. One lucky screenshot does not survive a channel review, and neither does one bad one. Every AI Visibility Tool Is Lying to You sets out the measurement problem in full.
More than one brand
One domain, many things to measure.
A domain-level score hides the parts of a business that move separately. Canonry scopes brands, regions, product lines, and business units as distinct targets, each with its own queries, competitors, and evidence. The write-up on one site with many markets walks through how that works.
Teams that need this as a dedicated application run Canonry Custom.
How the program runs
Three steps, then a cadence.
Canonry runs all three, or just the one your engineers have no sprint for. Already working with an SEO agency? We can sit alongside them, or go behind them white-label if they would rather own it.
- 01
Baseline
Score the pages that matter across 16 onsite factors, then record which buyer prompts return you, a competitor, or nothing.
- 02
Execute
Schema, crawler access, extractable structure, entity consistency, and off-site corroboration. We implement, or hand your engineers scoped tickets.
- 03
Report
Movement per prompt and per engine, plus AI crawler and referral traffic separated from ordinary traffic, on your review cadence.
Direct answers
Before you take this to a budget conversation.
Why does AI search need engineers and not just content people?
Because most of what decides a citation is not copy. It is valid JSON-LD, crawler access in robots.txt, server-side log analysis to separate AI crawlers from ordinary traffic, entity consistency across the sources engines retrieve from, HTML that survives extraction, and a measurement harness that runs prompts repeatedly because the answers are non-deterministic. Content matters, but it sits on top of that layer.
Our engineering team could build this. Why would we not?
They could. The question is whether AI search outranks the product roadmap this quarter, because that is the queue it joins. Teams that build it internally usually spend a quarter on tooling and methodology before producing a number they trust, then own the maintenance as engines change. Canonry has already paid that cost, and the platform is open source if you would rather run it yourself.
We already have an SEO agency. Does Canonry replace them?
Not necessarily. Your team can keep Canonry as the independent AI search measurement layer while the agency executes against it, or Canonry can run the AEO-specific technical, entity, and monitoring work that sits outside a standard SEO retainer. If the agency would rather own the whole program, they can bring Canonry in as their white-label delivery team instead.
How do we report this to leadership?
Per prompt and per engine across repeated runs, not single screenshots, because AI answers are non-deterministic and vary by model and run. You get cited, mentioned, or absent, which competitors appeared, which pages were used as sources, and how each moved since the last baseline.
Does this work across multiple brands or regions?
Yes, and that is usually where a shared dashboard breaks down. A domain-level score hides the parts of a business that need measuring separately. Canonry Custom scopes brands, regions, product lines, or business units as distinct targets, each keeping its own evidence.
AEO for marketing teams
Borrow the engineering team. Start with one URL.
No email required for the first check. Most audits finish in 5 to 15 seconds.