Why switch from Scrunch AI
Scrunch AI reports on how AI agents experience your brand across properties. This guide covers moving from that reporting to the engineering work that changes what the report says — including the agent-accessibility issues it surfaces.
Arise GEO vs Scrunch AI
Side-by-side table on scope, model, pricing shape and guarantees — including where Scrunch AI is the better buy. Read the comparison →
If you are standardising AI-visibility measurement across a portfolio of brands and the platform is the reporting backbone, keep it. Diagnose and fix underneath it rather than replacing it.
Signals it is time to move
- →Agent accessibility issues are reported repeatedly and never resolved.
- →Multiple properties show the same structural defect, indicating a template or platform cause.
- →Reporting is enterprise-grade and remediation has no owner.
- →Procurement cycles mean the platform renews faster than anything gets fixed.
The migration, step by step
Roughly six weeks end to end. Steps run in order; the change freeze overlaps the handover deliberately.
- Step 01You · Day 1–2
Export the probe history and the query set
Your tracked prompt list is the asset, not the subscription. Export every tracked query, the engines each was run against, the citation and mention history, competitor share-of-voice, and any sentiment scoring. Keep it as CSV — this becomes the continuity baseline so the switch does not reset your trend line to zero.
- Step 02Us + you · Week 1
Audit the query set for commercial relevance
Most AI-visibility subscriptions end up tracking 30 to 50 vanity prompts containing the brand name. Cut those. Rebuild the set around 100 to 300 unbranded buying questions a real prospect would type — comparison, 'best X for Y', pricing, integration and problem-first phrasings. Fewer than 100 and normal model variance swamps the signal.
- Step 03Us · Week 1–3
Establish why you are not being cited
A monitoring tool tells you the citation rate; it cannot tell you the cause. We check the three usual ones: the target pages are not retrievable or not indexed, the brand entity is ambiguous to the models, or the content carries no extractable answer passages. The remediation is completely different in each case.
- Step 04Us · Week 2–4
Fix technical eligibility before touching content
Confirm AI crawlers are not blocked in robots.txt or by the WAF and bot-management rules, that key pages render server-side or are prerendered, that canonicals are self-referencing, and that response times are sane for a fetch-and-summarise agent. Publishing into an unretrievable site produces a flat line.
- Step 05Us · Week 2
Deduplicate findings across the portfolio
When several brands run on shared templates or the same platform, one root cause appears as dozens of separate findings. Cluster them by root cause before building the backlog — a single template fix frequently closes a third of the open items across the whole portfolio.
- Step 06Us · Week 3–5
Anchor the entity graph
One canonical Organization node with a stable @id, sameAs pointing to the profiles you actually control, a named Person entity with knowsAbout for author-level pages, and Product or Service nodes referencing the Organization node rather than repeating it. Ambiguous identity is the most common reason a model summarises you without naming you.
- Step 07Us · Week 3–8
Ship atomic answer blocks on pages already indexed
For each high-value query, a 40 to 60 word self-contained answer directly beneath a question heading, on a page that already ranks and is topically relevant. This is consistently the fastest lever on citation share — movement usually shows within 30 to 60 days on long-tail and comparison queries.
- Step 08Us · Ongoing, from week 4
Re-probe against the imported baseline
Run the same query set, on the same engines, at the same cadence, several times per cycle to control for model output variance. Because you imported the history in step one, the comparison is genuinely like-for-like rather than a fresh start that flatters the new vendor.
What to keep, not bin
Risks and how we contain them
| Risk | Mitigation |
|---|---|
| The AI visibility trend line resets and you lose the comparison | Import the exported probe history as the baseline so the new measurement continues the same series. |
| Query set changes make before/after meaningless | Keep the original tracked queries running unchanged alongside the expanded set for one full cycle, then retire the vanity prompts. |
| Expecting citation movement in two weeks | Long-tail and comparison queries typically move in 30 to 60 days; head terms take one to two quarters. Anything faster is usually model variance rather than progress. |
| Portfolio-wide changes deployed at once | Pilot on one brand, verify index and citation impact for two to three weeks, then roll out across the portfolio. |
Frequently asked
What is agent experience and why does it matter?+
It is how AI crawlers and agents fetch, parse and act on your site. It matters for the same reason rendering matters: an agent that cannot retrieve or interpret the page cannot cite it or transact against it. It is a subset of the technical eligibility layer in any competent GEO audit.
Can you handle multiple brands at once?+
Yes. Multi-brand and multi-locale engagements are common, and clustering findings by root cause is usually where the first big win comes from on a portfolio.
Should we fix first or keep measuring?+
Fix first, then standardise measurement. Measuring a broken baseline precisely just documents the problem in higher resolution, and it delays the only thing that changes the number.
